arXiv 9 Nov 2021 · Econometrics
arXiv:2111.05277 · PDF · DOI · OpenAlex · Extracted main text
I propose kernel ridge regression estimators for nonparametric dose response curves and semiparametric treatment effects in the setting where an analyst has access to a selected sample rather than a random sample; only for select observations, the outcome is observed. I assume selection is as good as random conditional on treatment and a sufficiently rich set of observed covariates, where the covariates are allowed to cause treatment or be caused by treatment -- an extension of missingness-at-random (MAR). I propose estimators of means, increments, and distributions of counterfactual outcomes with closed form solutions in terms of kernel matrix operations, allowing treatment and covariates to be discrete or continuous, and low, high, or infinite dimensional. For the continuous treatment case, I prove uniform consistency with finite sample rates. For the discrete treatment case, I prove root-n consistency, Gaussian approximation, and semiparametric efficiency.
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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 | Michela Bia, Martin Huber, and Lukás Lafférs (2020) Double machine learning for sample selection models | 0.977 | 15 | 4 | 93% |
| 2 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.928 | 5 | 4 | 80% |
| 3 | Victor Chernozhukov, Whitney K Newey, and Rahul Singh (2021) A simple and general debiased machine learning theorem with finite sample guarantees self | 0.874 | 6 | 4 | 67% |
| 4 | Martin Huber (2012) Identification of average treatment effects in social experiments under alternative forms of attrition | 0.874 | 5 | 2 | 100% |
| 5 | Donald B Rubin (1976) Inference and missing data | 0.843 | 3 | 3 | 100% |
| 6 | Martin Huber (2014) Treatment evaluation in the presence of sample selection | 0.811 | 4 | 2 | 100% |
| 7 | Rahul Singh, Liyuan Xu, and Arthur Gretton (2020) Reproducing kernel methods for nonparametric and semiparametric treatment effects self | 0.794 | 16 | 7 | 50% |
| 8 | Rahul Singh, Liyuan Xu, and Arthur Gretton (2021) Kernel methods for multistage causal inference: Mediation analysis and dynamic treatment effects self | 0.773 | 13 | 7 | 46% |
| 9 | Alex Smola, Arthur Gretton, Le Song, and Bernhard Schölkopf (2007) A Hilbert space embedding for distributions | 0.737 | 3 | 3 | 67% |
| 10 | Rahul Singh, Maneesh Sahani, and Arthur Gretton (2019) Kernel instrumental variable regression self | 0.693 | 6 | 4 | 33% |
Showing the top 10 of 62 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Kernel methods for long term dose response curves | 0.405 | 1 | 1 |