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A Day in the Life of an Experimentation and Causal Inference Scientist @ Netflix

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/a-day-in-the-life-of-an-experimentation-and-causal-inference-scientist-netflix-388edfb77d21

Stephanie Lane, Wenjing Zheng, Mihir Tendulkar

Source credit: Netflix

Within the rapid expansion of data-related roles in the last decade, the title Data Scientist has emerged as an umbrella term for myriad skills and areas of business focus. What does this title mean within a given company, or even within a given industry? It can be hard to know from the outside. At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group.

Learning through data is in Netflix’s DNA. Our quasi-experimentation helps us constantly improve our streaming experience, giving our members fewer buffers and ever better video quality. We use A/B tests to introduce new product features, such as our daily Top 10 row that help our members discover their next favorite show. Our experimentation and causal inference focused data scientists help shape business decisions, product innovations, and engineering improvements across our service.

In this post, we discuss a day in the life of experimentation and causal inference data scientists at Netflix, interviewing some of our stunning colleagues along the way. We talked to scientists from areas like Payments & Partnerships, Content & Marketing Analytics Research, Content Valuation, Customer Service, Product Innovation, and Studio Production. You’ll read about their backgrounds, what best prepared them for their current role at Netflix, what they do in their day-to-day, and how Netflix contributes to their growth in their data science journey.

Who we are

One of the best parts of being a data scientist at Netflix is that there’s no one type of data scientist! We come from many academic backgrounds, including economics, radiotherapy, neuroscience, applied mathematics, political science, and biostatistics. We worked in different industries before joining Netflix, including tech, entertainment, retail, science policy, and research. These diverse and complementary backgrounds enrich the perspectives and technical toolkits that each of us brings to a new business question.

We’ll turn things over to introduce you to a few of our data scientists, and hear how they got here.

What brought you to the field of data science? Did you always know you wanted to do data science?

Roxy Du (Product Innovation)

[Roxy D.] A combination of interest, passion, and luck! While working on my PhD in political science, I realized my curiosity was always more piqued by methodological coursework, which led me to take as many stats/data science courses as I could. Later I enrolled in a data science program focused on helping academics transition to industry roles.

Reza Badri (Content Valuation)

[Reza B.] A passion for making informed decisions based on data. Working on my PhD, I was using optimization techniques to design radiotherapy fractionation schemes to improve the results of clinical practices. I wanted to learn how to better extract interesting insight from data, which led me to take several courses in statistics and machine learning. After my PhD, I started working as a data scientist at Target, where I built mathematical models to improve real-time pricing recommendation and ad serving engines.

Gwyn Bleikamp (Payments)

[Gwyn B.]: I’ve always loved math and statistics, so after college, I planned to become a statistician. I started working at a local payment processing company after graduation, where I built survival models to calculate lifetime value and experimented with them on our brand new big data stack. I was doing data science without realizing it.

What best prepared you for your current role at Netflix? Are there any experiences that particularly helped you bring a unique voice/point of view to Netflix?

David Cameron (Studio Production)

[David C.] I learned a lot about sizing up the potential impact of an opportunity (using back of the envelope math), while working as a management consultant after undergrad. This has helped me prioritize my work so that I’m spending most of my time on high-impact projects.

Aliki Mavromoustaki (Content & Marketing)

[Aliki M.] My academic credentials definitely helped on the technical side. Having a background in research also helps with critical thinking and being comfortable with ambiguity. Personally I value my teaching experiences the most, as they allowed me to improve the way I approach and break down problems effectively.

What we do at Netflix

But what does a day in the life of an experimentation/causal inference data scientist at Netflix actually look like? We work in cross-functional environments, in close collaboration with business, product and creative decision makers, engineers, designers, and consumer insights researchers. Our work provides insights and informs key decisions that improve our product and create more joy for our members. To hear more, we’ll hand you back over to our stunning colleagues.

Tell us about your business area and the type of stakeholders you partner with on a regular basis. How do you, as a data scientist, fill in the pieces between product, engineering, and design?

[Roxy D.] I partner with product managers to run AB experiments that drive product innovation. I collaborate with product managers, designers, and engineers throughout the lifecycle of a test, including ideation, implementation, analysis, and decision-making. Recently, we introduced a simple change in kids profiles that helps kids more easily find their rewatched titles. The experiment was conceived based on what we’d heard from members in consumer research, and it was very gratifying to address an underserved member need.

[David C.] There are several different flavors of data scientist in the Artwork and Video team. My specialties are on the Statistics and Optimization side. A recent favorite project was to determine the optimal number of images to create for titles. This was a fun project for me, because it combined optimization, statistics, understanding of reinforcement learning bandit algorithms, as well as general business sense, and it has far-reaching implications to the business.

What are your responsibilities as the data scientist in these projects? What technical skills do you draw on most?

[Gwyn B.] Data scientists can take on any aspect of an experimentation project. Some responsibilities I routinely have are: designing tests, metrics development and defining what success looks like, building data pipelines and visualization tools for custom metrics, analyzing results, and communicating final recommendations with broad teams. Coding with statistical software and SQL are my most widely used technical skills.

[David C.] One of the most important responsibilities I have is doing the exploratory data analysis of the counterfactual data produced by our bandit algorithms. These analyses have helped our stakeholders identify major opportunities, bugs and tighten up engineering pipelines. One of the most common analyses that I do is a look-back analysis on the explore-data. This data helps us analyze natural experiments and understand which type of images better introduce our content to our members.

Wenjing Zheng (Partnerships)
Stephanie Lane (Partnerships)

[Stephanie L. & Wenjing Z.] As data scientists in Partnerships, we work closely with our business development, partner marketing, and partner engagement teams to create the best possible experience of Netflix on every device. Our analyses help inform ways to improve certain product features (e.g., a Netflix row on your Smart TV) and consumer offers (e.g., getting Netflix as part of a bundled package), to provide the best experiences and value for our customers. But randomized, controlled experiments are not always feasible. We draw on technical expertise in varied forms of causal inference — interrupted time series designs, inverse probability weighting, and causal machine learning — to identify promising natural experiments, design quasi-experiments, and deliver insights. Not only do we own all steps of the analysis and communicate findings within Netflix, we often participate in discussions with external partners on how best to improve the product. Here, we draw on strong business context and communication to be most effective in our roles.

What non-technical skills do you draw on most?

[Aliki M.] Being able to adapt my communication style to work well with both technical and non-technical audiences. Building strong relationships with partners and working effectively in a team.

[Gwyn B.] Written communication is among the topmost valuable non-technical assets. Netflix is a memo-based culture, which means we spend a lot of time reading and writing. This is a primary way we share results and recommendations as well as solicit feedback on project ideas. Data Scientists need to be able to translate statistical analyses, test results, and significance into recommendations that the team can understand and action on.

How is working at Netflix different from where you’ve worked before?

[Reza B.] The Netflix culture makes it possible for me to continuously grow both technically and personally. Here, I have the opportunity to take risks and work on problems that I find interesting and impactful. Netflix is a great place for curious researchers that want to be challenged everyday by working on interesting problems. The tooling here is amazing, which made it easy for me to make my models available at scale across the company.

Mihir Tendulkar (Payments)

[Mihir T.] Each company has their own spin on data scientist responsibilities. At my previous company, we owned everything end-to-end: data discovery, cleanup, ETL, analysis, and modeling. By contrast, Netflix puts data infrastructure and quality control under the purview of specialized platform teams, so that I can focus on supporting my product stakeholders and improving experimentation methodologies. My wish-list projects are becoming a reality here: studying experiment interaction effects, quantifying the time savings of Bayesian inference, and advocating for Mindhunter Season 3.

[Stephanie L.] In my last role, I worked at a research think tank in the D.C. area, where I focused on experimentation and causal inference in national defense and science policy. What sets Netflix apart (other than the domain shift!) is the context-rich culture and broad dissemination of information. New initiatives and strategy bets are captured in memos for anyone in the company to read and engage in discourse. This context-rich culture enables me to rapidly absorb new business context and ultimately be a better thought partner to my stakeholders.

Data scientists at Netflix wear many hats. We work closely with business and creative stakeholders at the ideation stage to identify opportunities, formulate research questions, define success, and design studies. We partner with engineers to implement and debug experiments. We own all aspects of the analysis of a study (with help from our stellar data engineering and experimentation platform teams) and broadly communicate the results of our work. In addition to company-wide memos, we often bring our analytics point of view to lively cross-functional debates on roll-out decisions and product strategy. These responsibilities call for technical skills in statistics and machine learning, and programming knowledge in statistical software (R or Python) and SQL. But to be truly effective in our work, we also rely on non-technical skills like communication and collaborating in an interdisciplinary team.

You’ve now heard how our data scientists got here and what drives them to be successful at Netflix. But the tools of data science, as well as the data needs of a company, are constantly evolving. Before we wrap up, we’ll hand things over to our panel one more time to hear how they plan to continue growing in their data science journey at Netflix.

How are you looking to develop as a data scientist in the near future, and how does Netflix help you on that path?

[Reza B.] As a researcher, I like to continue growing both technically and non-technically; to keep learning, being challenged and work on impactful problems. Netflix gives me the opportunity to work on a variety of interesting problems, learn cutting-edge skills and be impactful. I am passionate about improving decision making through data, and Netflix gives me that opportunity. Netflix culture helps me receive feedback on my non-technical and technical skills continuously, providing helpful context for me to grow and be a better scientist.

[Aliki M.] True to our Netflix values, I am very curious and want to continue to learn, strengthen and expand my skill set. Netflix exposes me to interesting questions that require critical thinking from design to execution. I am surrounded by passionate individuals who inspire me and help me be better through their constructive feedback. Finally, my manager is highly aligned with me regarding my professional goals and looks for opportunities that fit my interests and passions.

[Roxy D.] I look forward to continuously growing on both the technical and non-technical sides. Netflix has been my first experience outside academia, and I have enjoyed learning about the impact and contribution of data science in a business environment. I appreciate that Netflix’s culture allows me to gain insights into various aspects of the business, providing helpful context for me to work more efficiently, and potentially with a larger impact.

As data scientists, we are continuously looking to add to our technical toolkit and to cultivate non-technical skills that drive more impact in our work. Working alongside stunning colleagues from diverse technical and business areas means that we are constantly learning from each other. Strong demand for data science across all business areas of Netflix affords us the ability to collaborate in new problem areas and develop new skills, and our leaders help us identify these opportunities to further our individual growth goals. The constructive feedback culture in Netflix is also key in accelerating our growth. Not only does it help us see blind spots and identify areas of improvement, it also creates a supportive environment where we help each other grow.

Learning more

Interested in learning more about data roles at Netflix? You’re in the right place! Check out our post on Analytics at Netflix to find out more about two other data roles at Netflix — Analytics Engineers and Data Visualization Engineers — who also drive business impact through data. You can search our open roles in Data Science and Engineering here. Our culture is key to our impact and growth: read about it here.


A Day in the Life of an Experimentation and Causal Inference Scientist @ Netflix was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Netflix at MIT CODE 2020

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/netflix-at-mit-code-2020-ad3745525218

Martin Tingley

In November, Netflix was a proud sponsor of the 2020 Conference on Digital Experimentation (CODE), hosted by the MIT Initiative on the Digital Economy. As well as providing sponsorship, Netflix data scientists were active participants, with three contributions.

Eskil Forsell and colleagues presented a poster describing Success stories from a democratized experimentation platform. Over the last few years, we’ve been Reimagining Experimentation Analysis at Netflix with an open platform that supports contributions of metrics, methods and visualizations. This poster, reproduced below, highlights some of the success stories we are now seeing, as data scientists across Netflix partner with our platform team to broaden the suite of methodologies we can support at scale. Ultimately, these successes support confident decision making from our experiments, and help Netflix deliver more joy to our members!

Simon Ejdemyr presented a talk describing how Netflix is exploring Low-latency multivariate Bayesian shrinkage in online experiments. This work is another example of the benefits of the open Experimentation Platform at Netflix, as we are able to research and implement new methods directly within our production environment, where we can assess their performance in real applications. In such empirical validations of our Bayesian implementation, we see meaningful improvements to statistical precision, including reductions in sign and magnitude errors that can be common to traditional approaches to identifying winning treatments.

Finally, Jeffrey Wong participated in a Practitioners Panel discussion with Lilli Dworkin (Facebook) and Ronny Kohavi (Airbnb), moderated by Dean Eckles. One theme of the discussion was the challenge of applying the cutting edge causal inference methods that are developed by academic researchers in the context of the highly scaled and automated experimentation platforms at major technology companies. To address these challenges, Netflix has made a deliberate investment in Computational Causal Inference, an interdisciplinary and collaborative approach to accelerating causal inference research and providing data-science-centric software that helps us address scaling issues.

CODE was a great opportunity for us to share the progress we’ve made at Netflix, and to learn from our colleagues from academe and industry. We are all looking forward to CODE 2021, and to engaging with the experimentation community throughout 2021.


Netflix at MIT CODE 2020 was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Computational Causal Inference at Netflix

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/computational-causal-inference-at-netflix-293591691c62

Jeffrey Wong, Colin McFarland

Every Netflix data scientist, whether their background is from biology, psychology, physics, economics, math, statistics, or biostatistics, has made meaningful contributions to the way Netflix analyzes causal effects. Scientists from these fields have made many advancements in causal effects research in the past few decades, spanning instrumental variables, forest methods, heterogeneous effects, time-dynamic effects, quantile effects, and much more. These methods can provide rich information for decision making, such as in experimentation platforms (“XP”) or in algorithmic policy engines.

We want to amplify the effectiveness of our researchers by providing them software that can estimate causal effects models efficiently, and can integrate causal effects into large engineering systems. This can be challenging when algorithms for causal effects need to fit a model, condition on context and possible actions to take, score the response variable, and compute differences between counterfactuals. Computation can explode and become overwhelming when this is done with large datasets, with high dimensional features, with many possible actions to choose from, and with many responses. In order to gain broad software integration of causal effects models, a significant investment in software engineering, especially in computation, is needed. To address the challenges, Netflix has been building an interdisciplinary field across causal inference, algorithm design, and numerical computing, which we now want to share with the rest of the industry as computational causal inference (CompCI). A whitepaper detailing the field can be found here.

Computational causal inference brings a software implementation focus to causal inference, especially in regards to high performance numerical computing. We are implementing several algorithms to be highly performant, with a low memory footprint. As an example, our XP is pivoting away from two sample t-tests to models that estimate average effects, heterogeneous effects, and time-dynamic treatment effects. These effects help the business understand the user base, different segments in the user base, and whether there are trends in segments over time. We also take advantage of user covariates throughout these models in order to increase statistical power. While this rich analysis helps to inform business strategy and increase member joy, the volume of the data demands large amounts of memory, and the estimation of the causal effects on such volume of data is computationally heavy.

In the past, the computations for covariate adjusted heterogeneous effects and time-dynamic effects were slow, memory heavy, hard to debug, a large source of engineering risk, and ultimately could not scale to many large experiments. Using optimizations from CompCI, we can estimate hundreds of conditional average effects and their variances on a dataset with 10 million observations in 10 seconds, on a single machine. In the extreme, we can also analyze conditional time dynamic treatment effects for hundreds of millions of observations on a single machine in less than one hour. To achieve this, we leverage a software stack that is completely optimized for sparse linear algebra, a lossless data compression strategy that can reduce data volume, and mathematical formulas that are optimized specifically for estimating causal effects. We also optimize for memory and data alignment.

This level of computing affords us a lot of luxury. First, the ability to scale complex models means we can deliver rich insights for the business. Second, being able to analyze large datasets for causal effects in seconds increases research agility. Third, analyzing data on a single machine makes debugging easy. Finally, the scalability makes computation for large engineering systems tractable, reducing engineering risk.

Computational causal inference is a new, interdisciplinary field we are announcing because we want to build it collectively with the broader community of experimenters, researchers, and software engineers. The integration of causal inference into engineering systems can lead to large amounts of new innovation. Being an interdisciplinary field, it truly requires the community of local, domain experts to unite. We have released a whitepaper to begin the discussion. There, we describe the rising demand for scalable causal inference in research and in software engineering systems. Then, we describe the state of common causal effects models. Afterwards, we describe what we believe can be a good software framework for estimating and optimizing for causal effects.

Finally, we close the CompCI whitepaper with a series of open challenges that we believe require an interdisciplinary collaboration, and can unite the community around. For example:

  1. Time dynamic treatment effects are notoriously hard to scale. They require a panel of repeated observations, which generate large datasets. They also contain autocorrelation, creating complications for estimating the variance of the causal effect. How can we make the computation for the time-dynamic treatment effect, and its distribution, more scalable?
  2. In machine learning, specifying a loss function and optimizing it using numerical methods allows a developer to interact with a single, umbrella framework that can span several models. Can such an umbrella framework exist to specify different causal effects models in a unified way? For example, could it be done through the generalized method of moments? Can it be computationally tractable?
  3. How should we develop software that understands if a causal parameter is identified? A solution to this helps to create software that is safe to use, and can provide safe, programmatic access to the analysis of causal effects. We believe there are many edge cases in identification that require an interdisciplinary group to solve.

We hope this begins the discussion, and over the coming months we will be sharing more on the research we have done to make estimation of causal effects performant. There are still many more challenges in the field that are not listed here. We want to form a community spanning experimenters, researchers, and software engineers to learn about problems and solutions together. If you are interested in being part of this community, please reach us at [email protected]


Computational Causal Inference at Netflix was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Design Principles for Mathematical Engineering in Experimentation Platform at Netflix

Post Syndicated from Netflix Technology Blog original https://medium.com/netflix-techblog/design-principles-for-mathematical-engineering-in-experimentation-platform-15b3ea143b1f?source=rss----2615bd06b42e---4

Jeffrey Wong, Senior Modeling Architect, Experimentation Platform
Colin McFarland, Director, Experimentation Platform

At Netflix, we have data scientists coming from many backgrounds such as neuroscience, statistics and biostatistics, economics, and physics; each of these backgrounds has a meaningful contribution to how experiments should be analyzed. To unlock these innovations we are making a strategic choice that our focus should be geared towards developing the surrounding infrastructure so that scientists’ work can be easily absorbed into the wider Netflix Experimentation Platform. There are 2 major challenges to succeed in our mission:

  1. We want to democratize the platform and create a contribution model: with a developer and production deployment experience that is designed for data scientists and friendly to the stacks they use.
  2. We have to do it at Netflix’s scale: For hundreds of millions of users across hundreds of concurrent tests, spanning many deployment strategies from traditional A/B experiments, to evolving areas like quasi experiments.

Mathematical engineers at Netflix in particular work on the scalability and engineering of models that estimate treatment effects. They develop scientific libraries that scientists can apply to analyze experiments, and also contribute to the engineering foundations to build a scientific platform where new research can graduate to. In order to produce software that improves a scientist’s productivity we have come up with the following design principles.

1. Composition

Data Science is a curiosity driven field, and should not be unnecessarily constrained[1]. We support data scientists to have freedom to explore research in any new direction. To help, we provide software autonomy for data scientists by focusing on composition, a design principle popular in data science software like ggplot2 and dplyr[2]. Composition exposes a set of fundamental building blocks that can be assembled in various combinations to solve complex problems. For example, ggplot2 provides several lightweight functions like geom_bar, geom_point, geom_line, and theme, that allow the user to assemble custom visualizations; every graph whether simple or complex can be composed of small, lightweight ggplot2 primitives.

In the democratization of the experimentation platform we also want to allow custom analysis. Since converting every experiment analysis into its own function for the experimentation platform is not scalable, we are making the strategic bet to invest in building high quality causal inference primitives that can be composed into an arbitrarily complex analysis. The primitives include a grammar for describing the data generating process, generic counterfactual simulations, regression, bootstrapping, and more.

2. Performance

If our software is not performant it could limit adoption, subsequent innovation, and business impact. This will also make graduating new research into the experimentation platform difficult. Performance can be tackled from at least three angles:

A) Efficient computation

We should leverage the structure of the data and of the problem as much as possible to identify the optimal compute strategy. For example, if we want to fit ridge regression with various different regularization strengths we can do an SVD upfront and express the full solution path very efficiently in terms of the SVD.

B) Efficient use of memory

We should optimize for sparse linear algebra. When there are many linear algebra operations, we should understand them holistically so that we can optimize the order of operations and not materialize unnecessary intermediate matrices. When indexing into vectors and matrices, we should index contiguous blocks as much as possible to improve spatial locality[3].

C) Compression

Algorithms should be able to work on raw data as well as compressed data. For example, regression adjustment algorithms should be able to use frequency weights, analytic weights, and probability weights[4]. Compression algorithms can be lossless, or lossy with a tuning parameter to control the loss of information and impact on the standard error of the treatment effect.

3. Graduation

We need a process for graduating new research into the experimentation platform. The end to end data science cycle usually starts with a data scientist writing a script to do a new analysis. If the script is used several times it is rewritten into a function and moved into the Analysis Library. If performance is a concern, it can be refactored to build on top of high performance causal inference primitives made by mathematical engineers. This is the first phase of graduation.

The first phase will have a lot of iterations. The iterations go in both directions: data scientists can promote functions into the library, but they can also use functions from the library in their analysis scripts.

The second phase interfaces the Analysis Library with the rest of the experimentation ecosystem. This is the promotion of the library into the Statistics Backend, and negotiating engineering contracts for input into the Statistics Backend and output from the Statistics Backend. This can be done in an experimental notebook environment, where data scientists can demonstrate end to end what their new work will look like in the platform. This enables them to have conversations with stakeholders and other partners, and get feedback on how useful the new features are. Once the concepts have been proven in the experimental environment, the new research can graduate into the production experimentation platform. Now we can expose the innovation to a large audience of data scientists, engineers and product managers at Netflix.

4. Reproducibility

Reproducibility builds trustworthiness, transparency, and understanding for the platform. Developers should be able to reproduce an experiment analysis report outside of the platform using only the backend libraries. The ability to replicate, as well as rerun the analysis programmatically with different parameters is crucial for agility.

5. Introspection

In order to get data scientists involved with the production ecosystem, whether for debugging or innovation, they need to be able to step through the functions the platform is calling. This level of interaction goes beyond reproducibility. Introspectable code allows data scientists to check data, the inputs into models, the outputs, and the treatment effect. It also allows them to see where the opportunities are to insert new code. To make this easy we need to understand the steps of the analysis, and expose functions to see intermediate steps. For example we could break down the analysis of an experiment as

  • Compose data query
  • Retrieve data
  • Preprocess data
  • Fit treatment effect model
  • Use treatment effect model to estimate various treatment effects and variances
  • Post process treatment effects, for example with multiple hypothesis correction
  • Serialize analysis results to send back to the Experimentation Platform

It is difficult for a data scientist to step through the online analysis code. Our path to introspectability is to power the analysis engine using python and R, a stack that is easy for a data scientist to step through. By making the analysis engine a python and R library we will also gain reproducibility.

6. Scientific Code in Production and in Offline Environments

In the causal inference domain data scientists tend to write code in python and R. We intentionally are not rewriting scientific functions into a new language like Java, because that will render the library useless for data scientists since they cannot integrate optimized functions back into their work. Rewriting poses reproducibility challenges since the python/R stack would need to match the Java stack. Introspection is also more difficult because the production code requires a separate development environment.

We choose to develop high performance scientific primitives in C++, which can easily be wrapped into both python and R, and also delivers on highly performant, production quality scientific code. In order to support the diversity of the data science teams and offer first class support for hybrid stacks like python and R, we standardize data on the Apache Arrow format in order to facilitate data exchange to different statistics languages with minimal overhead.

7. Well Defined Point of Entry, Well Defined Point of Exit

Our causal inference primitives are developed in a pure, scientific library, without business logic. For example, regression can be written to accept a feature matrix and a response vector, without any specific experimentation data structures. This makes the library portable, and allows data scientists to write extensions that can reuse the highly performant statistics functions for their own adhoc analysis. It is also portable enough for other teams to share.

Since these scientific libraries are decoupled from business logic, they will always be sandwiched in any engineering platform; upstream will have a data layer, and downstream will have a visualization and interpretation layer. To facilitate a smooth data flow, we need to design simple connectors. For example, all analyses need to receive data and a description of the data generating process. By focusing on composition, an arbitrary analysis can be constructed by layering causal analysis primitives on top of that starting point. Similarly, the end of an analysis will always consolidate into one data structure. This simplifies the workflow for downstream consumers so that they know what data type to consume.

Next Steps

We are actively developing high performance software for regression, heterogeneous treatment effects, longitudinal studies and much more for the Experimentation Platform at Netflix. We aim to accelerate research in causal inference methodology, expedite product innovation, and ultimately bring the best experience and delight to our members. This is an ongoing journey, and if you are passionate about our exciting work, join our all-star team!


Design Principles for Mathematical Engineering in Experimentation Platform at Netflix was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.