Harsh Parikh, Cynthia Rudin, Alexander Volfovsky
arXiv 18 Nov 2018 · Statistics — Methodology · 5 citations (OpenAlex)
arXiv:1811.07415 · PDF · DOI · OpenAlex · Extracted main text
We introduce a flexible framework that produces high-quality almost-exact matches for causal inference. Most prior work in matching uses ad-hoc distance metrics, often leading to poor quality matches, particularly when there are irrelevant covariates. In this work, we learn an interpretable distance metric for matching, which leads to substantially higher quality matches. The learned distance metric stretches the covariate space according to each covariate's contribution to outcome prediction: this stretching means that mismatches on important covariates carry a larger penalty than mismatches on irrelevant covariates. Our ability to learn flexible distance metrics leads to matches that are interpretable and useful for the estimation of conditional average treatment effects.
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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 | T. Wang, M. Morucci, M. U. Awan, Y. Liu, S. Roy, C. Rudin, and A. Vo… (2021) FLAME: A fast large-scale almost matching exactly approach to causal inference | 0.928 | 4 | 4 | 100% |
| 2 | A. Dieng, Y. Liu, S. Roy, C. Rudin, and A. Volfovsky (2019) Interpretable almost-exact matching for causal inference | 0.843 | 3 | 3 | 100% |
| 3 | R. J. LaLonde (1986) Evaluating the Econometric Evaluations of Training Programs with Experimental Data | 0.811 | 4 | 2 | 100% |
| 4 | B. B. Hansen (2008) The prognostic analogue of the propensity score | 0.737 | 3 | 3 | 67% |
| 5 | R. H. Dehejia and S. Wahba (1999) Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs | 0.737 | 3 | 2 | 100% |
| 6 | A. Abadie and G. W. Imbens (2006) Large sample properties of matching estimators for average treatment effects | 0.644 | 2 | 2 | 100% |
| 7 | J. H. Friedman (1991) Multivariate adaptive regression splines | 0.585 | 3 | 1 | 100% |
| 8 | P. R. Rosenbaum and D. B. Rubin (1983) The central role of the propensity score in observational studies for causal effects | 0.511 | 2 | 2 | 50% |
| 9 | A. Bellet and A. Habrard (2015) Robustness and generalization for metric learning | 0.511 | 2 | 1 | 100% |
| 10 | G. W. Imbens (2004) Nonparametric estimation of average treatment effects under exogeneity: A review | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 44 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Variable Importance Matching for Causal Inference | 1.000 | 11 | 7 |
| 2 | A Double Machine Learning Approach for Combining Experimental and Observational Studies | 0.843 | 5 | 3 |