Masahiro Kato, Masaaki Imaizumi
arXiv 25 Oct 2023 · Econometrics · 3 citations (OpenAlex)
arXiv:2310.16819 · PDF · DOI · OpenAlex · Extracted main text
In causal inference about two treatments, Conditional Average Treatment Effects (CATEs) play an important role as a quantity representing an individualized causal effect, defined as a difference between the expected outcomes of the two treatments conditioned on covariates. This study assumes two linear regression models between a potential outcome and covariates of the two treatments and defines CATEs as a difference between the linear regression models. Then, we propose a method for consistently estimating CATEs even under high-dimensional and non-sparse parameters. In our study, we demonstrate that desirable theoretical properties, such as consistency, remain attainable even without assuming sparsity explicitly if we assume a weaker assumption called implicit sparsity originating from the definition of CATEs. In this assumption, we suppose that parameters of linear models in potential outcomes can be divided into treatment-specific and common parameters, where the treatment-specific parameters take difference values between each linear regression model, while the common parameters remain identical. Thus, in a difference between two linear regression models, the common parameters disappear, leaving only differences in the treatment-specific parameters. Consequently, the non-zero parameters in CATEs correspond to the differences in the treatment-specific parameters. Leveraging this assumption, we develop a Lasso regression method specialized for CATE estimation and present that the estimator is consistent. Finally, we confirm the soundness of the proposed method by simulation studies.
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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 | X Nie and S Wager (2020) Quasi-oracle estimation of heterogeneous treatment effects | 0.843 | 5 | 3 | 60% |
| 2 | James J. Heckman, Hidehiko Ichimura, and Petra E. Todd (1997) Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme | 0.737 | 3 | 3 | 67% |
| 3 | Stefan Wager and Susan Athey (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.737 | 3 | 3 | 67% |
| 4 | Sören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu (2019) Metalearners for estimating heterogeneous treatment effects using machine learning | 0.693 | 8 | 2 | 50% |
| 5 | Jerzy Neyman (1923) Sur les applications de la theorie des probabilites aux experiences agricoles: Essai des principes | 0.644 | 2 | 2 | 100% |
| 6 | Donald B. Rubin (1974) Estimating causal effects of treatments in randomized and nonrandomized studies | 0.644 | 2 | 2 | 100% |
| 7 | R. Tibshirani (1996) Regression shrinkage and selection via the lasso | 0.644 | 2 | 2 | 100% |
| 8 | Sara van de Geer, Peter Bühlmann, Ya’acov Ritov, and Ruben Dezeure (2014) On asymptotically optimal confidence regions and tests for high-dimensional models | 0.606 | 9 | 3 | 22% |
| 9 | Jennifer L. Hill (2011) Bayesian nonparametric modeling for causal inference | 0.550 | 6 | 3 | 17% |
| 10 | Peter Bühlmann and Sara van de Geer (2011) Statistics for high-dimensional data | 0.529 | 9 | 2 | 22% |
Showing the top 10 of 78 scored citations.