arXiv 9 Sep 2022 · Econometrics · publishedJournal of Econometrics (2024) · 2 citations (OpenAlex)
arXiv:2209.04329 · PDF · DOI · OpenAlex · Extracted main text
We propose a method for estimation and inference for bounds for heterogeneous causal effect parameters in general sample selection models where the treatment can affect whether an outcome is observed and no exclusion restrictions are available. The method provides conditional effect bounds as functions of policy relevant pre-treatment variables. It allows for conducting valid statistical inference on the unidentified conditional effects. We use a flexible debiased/double machine learning approach that can accommodate non-linear functional forms and high-dimensional confounders. Easily verifiable high-level conditions for estimation, misspecification robust confidence intervals, and uniform confidence bands are provided as well. We re-analyze data from a large scale field experiment on Facebook on counter-attitudinal news subscription with attrition. Our method yields substantially tighter effect bounds compared to conventional methods and suggests depolarization effects for younger users.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lee, D. S (2009) Training, wages, and sample selection: Estimating sharp bounds on treatment effects | 1.000 | 7 | 4 | 100% |
| 2 | Zhang, J. L. and Rubin, D. B (2003) death | 1.000 | 6 | 3 | 100% |
| 3 | Semenova, V (2023) Generalized lee bounds | 0.961 | 18 | 5 | 89% |
| 4 | Belloni, A., Chernozhukov, V., Chetverikov, D., and Kato, K (2015) Some new asymptotic theory for least squares series: Pointwise and uniform results | 0.961 | 9 | 4 | 89% |
| 5 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.950 | 7 | 5 | 86% |
| 6 | Semenova, V. and Chernozhukov, V (2021) Debiased machine learning of conditional average treatment effects and other causal functions | 0.950 | 7 | 5 | 86% |
| 7 | Andrews, D. W. K. and Kwon, S (2023) Misspecified Moment Inequality Models: Inference and Diagnostics | 0.909 | 8 | 4 | 75% |
| 8 | Stoye, J (2020) A simple, short, but never-empty confidence interval for partially identified parameters | 0.894 | 7 | 4 | 71% |
| 9 | Levy, R (2021) Social media, news consumption, and polarization: Evidence from a field experiment | 0.874 | 18 | 2 | 100% |
| 10 | Heiler, P. and Knaus, M (2021) Effect or treatment heterogeneity? Policy evaluation with aggregated and disaggregated treatments self | 0.737 | 3 | 3 | 67% |
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