Computational Causal Inference at Netflix | by Netflix Technology Blog | Aug, 2020


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.



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