Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page
arXiv 23 Feb 2023 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2302.11715 · PDF · DOI · OpenAlex · Extracted main text
Our goal is to produce methods for observational causal inference that are auditable, easy to troubleshoot, accurate for treatment effect estimation, and scalable to high-dimensional data. We describe a general framework called Model-to-Match that achieves these goals by (i) learning a distance metric via outcome modeling, (ii) creating matched groups using the distance metric, and (iii) using the matched groups to estimate treatment effects. Model-to-Match uses variable importance measurements to construct a distance metric, making it a flexible framework that can be adapted to various applications. Concentrating on the scalability of the problem in the number of potential confounders, we operationalize the Model-to-Match framework with LASSO. We derive performance guarantees for settings where LASSO outcome modeling consistently identifies all confounders (importantly without requiring the linear model to be correctly specified). We also provide experimental results demonstrating the method's auditability, accuracy, and scalability as well as extensions to more general nonparametric outcome modeling.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Harsh Parikh, Alexander Volfovsky, and Cynthia Rudin (2022) Malts: Matching after learning to stretch self | 1.000 | 11 | 7 | 100% |
| 2 | Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, and Ale… (2020) Adaptive hyper-box matching for interpretable individualized treatment effect estimation self | 1.000 | 5 | 4 | 100% |
| 3 | Tianyu Wang, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky (2017) FLAME: A fast large-scale almost matching exactly approach to causal inference self | 0.928 | 4 | 3 | 100% |
| 4 | Carlos Carvalho, Avi Feller, Jared Murray, Spencer Woody, and David… (1907) Assessing treatment effect variation in observational studies: Results from a data challenge, 2019 | 0.874 | 7 | 2 | 100% |
| 5 | Yue Zhang, Soumya Ray, and Weihong Guo (2016) On the consistency of feature selection with lasso for non-linear targets | 0.843 | 3 | 3 | 100% |
| 6 | Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Paul Oka, M… (2019) EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation | 0.644 | 4 | 1 | 100% |
| 7 | Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, and Alexander Vol… (2019) Interpretable almost-exact matching for causal inference self | 0.644 | 2 | 2 | 100% |
| 8 | Harsh Parikh, Kentaro Hoffman, Haoqi Sun, Sahar F. Zafar, Wendong Ge… (2022) Effects of epileptiform activity on discharge outcome in critically ill patients: A retrospective cross-sectional study self | 0.644 | 2 | 2 | 100% |
| 9 | Ruoqi Yu, Dylan S. Small, David Harding, José Aveldanes, and Paul R.… (2021) Optimal matching for observational studies that integrate quantitative and qualitative research | 0.644 | 2 | 2 | 100% |
| 10 | Alexis Diamond and Jasjeet S. Sekhon (2013) Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational st… | 0.644 | 2 | 2 | 100% |
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