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	<title>causal-inference &#8211; Noise</title>
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		<title>Round 2: A Survey of Causal Inference Applications at Netflix</title>
		<link>https://noise.getoto.net/2024/06/06/round-2-a-survey-of-causal-inference-applications-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 06 Jun 2024 20:10:54 +0000</pubDate>
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					<description><![CDATA[At Netflix, we want to ensure that every current and future member finds content that thrills them today and excites them to come back for more. Causal inference is an essential part of the value that Data Science and Engineering adds towards this miss...]]></description>
		
		
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		<title>Causal Machine Learning for Creative Insights</title>
		<link>https://noise.getoto.net/2023/11/25/causal-machine-learning-for-creative-insights/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Sat, 25 Nov 2023 01:27:21 +0000</pubDate>
				<category><![CDATA[causal-inference]]></category>
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					<description><![CDATA[A framework to identify the causal impact of successful visual components.By Billur Engin, Yinghong Lan, Grace Tang, Cristina Segalin, Kelli Griggs, Vi IyengarIntroductionAt Netflix, we want our viewers to easily find TV shows and movies that resonate ...]]></description>
		
		
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		<title>A Survey of Causal Inference Applications at Netflix</title>
		<link>https://noise.getoto.net/2022/05/21/a-survey-of-causal-inference-applications-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Sat, 21 May 2022 15:02:49 +0000</pubDate>
				<category><![CDATA[causal-inference]]></category>
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					<description><![CDATA[At Netflix, we want to entertain the world through creating engaging content and helping members discover the titles they will love. Key to that is understanding causal effects that connect changes we make in the product to indicators of member joy.To ...]]></description>
		
		
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		<title>Netflix: A Culture of Learning</title>
		<link>https://noise.getoto.net/2022/01/25/netflix-a-culture-of-learning/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 25 Jan 2022 16:23:45 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Colin McFarland, Mihir Tendulkar, and Travis BrooksThis is the last post in an overview series on experimentation at Netflix. Need to catch up? Earlier posts covered the basics of A/B te...]]></description>
		
		
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		<title>Experimentation is a major focus of Data Science across Netflix</title>
		<link>https://noise.getoto.net/2022/01/11/experimentation-is-a-major-focus-of-data-science-across-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 11 Jan 2022 16:09:13 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[decision-making]]></category>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Colin McFarland, Andy Rhines, Sophia Liu, Mihir Tendulkar, Kevin Mercurio, Veronica Hannan, Ting-Po LeeEarlier posts in this series covered the basics of A/B tests (Part 1 and Part 2 ), ...]]></description>
		
		
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		<title>Building confidence in a decision</title>
		<link>https://noise.getoto.net/2021/11/15/building-confidence-in-a-decision/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 15 Nov 2021 16:20:05 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Michael Lindon, and Colin McFarlandThis is the fifth post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catc...]]></description>
		
		
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		<title>Interpreting A/B test results: false negatives and power</title>
		<link>https://noise.getoto.net/2021/10/26/interpreting-a-b-test-results-false-negatives-and-power/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 26 Oct 2021 15:45:24 +0000</pubDate>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis is the fourth post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catch up? Have a lo...]]></description>
		
		
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		<title>Interpreting A/B test results: false positives and statistical significance</title>
		<link>https://noise.getoto.net/2021/10/07/interpreting-a-b-test-results-false-positives-and-statistical-significance/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 07 Oct 2021 15:11:37 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis is the third post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catch up? Have a loo...]]></description>
		
		
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		<title>What is an A/B Test?</title>
		<link>https://noise.getoto.net/2021/09/22/what-is-an-a-b-test/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 22 Sep 2021 15:54:33 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis is the second post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. See here for Part 1: Decisi...]]></description>
		
		
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		<title>Decision Making at Netflix</title>
		<link>https://noise.getoto.net/2021/09/07/decision-making-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 07 Sep 2021 15:47:48 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[decision-making]]></category>
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					<description><![CDATA[Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis introduction is the first in a multi-part series on how Netflix uses A/B tests to make decisions that continuously improve our products, so we can deliver more jo...]]></description>
		
		
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		<title>A Day in the Life of an Experimentation and Causal Inference Scientist @ Netflix</title>
		<link>https://noise.getoto.net/2021/03/03/a-day-in-the-life-of-an-experimentation-and-causal-inference-scientist-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 03 Mar 2021 00:45:43 +0000</pubDate>
				<category><![CDATA[ab-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
		<category><![CDATA[Data Science]]></category>
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					<description><![CDATA[Stephanie Lane, Wenjing Zheng, Mihir TendulkarSource credit: NetflixWithin 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 t...]]></description>
		
		
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		<title>Netflix at MIT CODE 2020</title>
		<link>https://noise.getoto.net/2020/12/16/netflix-at-mit-code-2020/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 16 Dec 2020 19:38:44 +0000</pubDate>
				<category><![CDATA[a-b-testing]]></category>
		<category><![CDATA[causal-inference]]></category>
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					<description><![CDATA[Martin TingleyIn 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, wi...]]></description>
		
		
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