arXiv 1 Jul 2025 · Econometrics
arXiv:2507.01202 · PDF · DOI · OpenAlex · Extracted main text
When using observational causal models, practitioners often want to disentangle the effects of many related, partially-overlapping treatments. Examples include estimating treatment effects of different marketing touchpoints, ordering different types of products, or signing up for different services. Common approaches that estimate separate treatment coefficients are too noisy for practical decision-making. We propose a computationally light model that uses a customized ridge regression to move between a heterogeneous and a homogenous model: it substantially reduces MSE for the effects of each individual sub-treatment while allowing us to easily reconstruct the effects of an aggregated treatment. We demonstrate the properties of this estimator in theory and simulation, and illustrate how it has unlocked targeted decision-making at Wayfair.
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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 | Nie, X. and S. Wager (2017) Quasi-Oracle Estimation of Heterogeneous Treatment Effects | 0.644 | 2 | 2 | 100% |
| 2 | Chernozhukov, V., C. Hansen, N. Kallus, M. Spindler, and V. Syrgkanis (2024) Applied Causal Inference Powered by ML and AI | 0.511 | 2 | 1 | 100% |
| 3 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2016) Double/Debiased Machine Learning for Treatment and Causal Parameters | 0.511 | 2 | 1 | 100% |
| 4 | Gelman, A., J. Carlin, H. Stern, D. Dunson, A. Vehtari, and D. Rubin (2013) Bayesian Data Analysis (3rd ed.) | 0.405 | 1 | 1 | 100% |
| 5 | Mahajan, D., I. Mitliagkas, B. Neal, and V. Syrgkanis (2022) Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation | 0.405 | 1 | 1 | 100% |
| 6 | Kennedy, E. H (2020) Towards optimal doubly robust estimation of heterogeneous causal effects | 0.405 | 1 | 1 | 100% |
| 7 | Lal, A (2024) Does Regression Produce Representative Causal Rankings? | 0.405 | 1 | 1 | 100% |
| 8 | Athey, S. and G. Imbens (2025) Identification of Average Treatment Effects in Nonparametric Panel Models | 0.405 | 1 | 1 | 100% |
| 9 | Künzel, S. R., J. S. Sekhon, P. J. Bickel, and B. Yu (2019) Metalearners for estimating heterogeneous treatment effects using machine learning | 0.405 | 1 | 1 | 100% |
Showing the top 9 of 9 scored citations.