Learning Causal Effects From Many Randomized Experiments Using Regularized Instrumental Variables
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p699-peysakhovich.pdf
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1.68 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Peysakhovich, Alexander
Eckles, Dean Griffin
Date Issued
2018
Journal
Proceedings of the 2018 World Wide Web Conference on World Wide Web
Publisher
ACM Press
Citation
Peysakhovich, Alexander and Dean Eckles. “Learning Causal Effects From Many Randomized Experiments Using Regularized Instrumental Variables.” Proceedings of the 2018 World Wide Web Conference on World Wide Web, April 2018, Lyon, France, ACM Press, 2018.
Version
Final published version
Abstract
Scientific and business practices are increasingly resulting in large collections of randomized experiments. Analyzed together multiple experiments can tell us things that individual experiments cannot. We study how to learn causal relationships between variables from the kinds of collections faced by modern data scientists: the number of experiments is large, many experiments have very small effects, and the analyst lacks metadata (e.g., descriptions of the interventions). We use experimental groups as instrumental variables (IV) and show that a standard method (two-stage least squares) is biased even when the number of experiments is infinite. We show how a sparsity-inducing l0 regularization can (in a reversal of the standard bias--variance tradeoff) reduce bias (and thus error) of interventional predictions. We are interested in estimating causal effects, rather than just predicting outcomes, so we also propose a modified cross-validation procedure (IVCV) to feasibly select the regularization parameter. We show, using a trick from Monte Carlo sampling, that IVCV can be done using summary statistics instead of raw data. This makes our full procedure simple to use in many real-world applications.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3178876.3186151
