Kazuhiko Shinoda, Takahiro Hoshino
arXiv 26 Dec 2022 · Econometrics
arXiv:2212.13145 · PDF · DOI · OpenAlex · Extracted main text
In various fields of data science, researchers are often interested in estimating the ratio of conditional expectation functions (CEFR). Specifically in causal inference problems, it is sometimes natural to consider ratio-based treatment effects, such as odds ratios and hazard ratios, and even difference-based treatment effects are identified as CEFR in some empirically relevant settings. This chapter develops the general framework for estimation and inference on CEFR, which allows the use of flexible machine learning for infinite-dimensional nuisance parameters. In the first stage of the framework, the orthogonal signals are constructed using debiased machine learning techniques to mitigate the negative impacts of the regularization bias in the nuisance estimates on the target estimates. The signals are then combined with a novel series estimator tailored for CEFR. We derive the pointwise and uniform asymptotic results for estimation and inference on CEFR, including the validity of the Gaussian bootstrap, and provide low-level sufficient conditions to apply the proposed framework to some specific examples. We demonstrate the finite-sample performance of the series estimator constructed under the proposed framework by numerical simulations. Finally, we apply the proposed method to estimate the causal effect of the 401(k) program on household assets.
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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 | Shinoda, K. and Hoshino, T (2022) Estimation of local average treatment effect by data combination self | 1.000 | 10 | 5 | 100% |
| 2 | Yamane, I., Yger, F., Atif, J. and Sugiyama, M (2018) Uplift modeling from separate labels | 1.000 | 9 | 4 | 100% |
| 3 | Singh, R. and Sun, L (2019) Automatic debiased machine learning for instrumental variable models of complier treatment effects | 0.874 | 5 | 2 | 100% |
| 4 | Semenova, V. and Chernozhukov, V (2021) Debiased machine learning of conditional average treatment effects and other causal functions | 0.822 | 9 | 4 | 56% |
| 5 | Yadlowsky, S., Pellegrini, F., Lionetto, F., Braune, S. and Tian, L (2021) Estimation and validation of ratio-based conditional average treatment effects using observational data | 0.737 | 3 | 2 | 100% |
| 6 | Belloni, A., Chernozhukov, V., Chetverikov, D. and Kato, K (2015) Some new asymptotic theory for least squares series: Pointwise and uniform results | 0.659 | 14 | 3 | 29% |
| 7 | Liang, M. and Yu, M (2020) Relative contrast estimation and inference for treatment recommendation | 0.644 | 3 | 2 | 67% |
| 8 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.644 | 2 | 2 | 100% |
| 9 | Chernozhukov, V., Newey, W. K. and Singh, R (2022) Automatic debiased machine learning of causal and structural effects | 0.644 | 2 | 2 | 100% |
| 10 | Chernozhukov, V., Newey, W., Quintas-Martńez, V. M. and Syrgkanis, V (2022) RieszNet and ForestRiesz: Automatic debiased machine learning with neural nets and random forests | 0.644 | 2 | 2 | 100% |
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