Facundo Argañaraz, Juan Carlos Escanciano
arXiv 31 Oct 2024 · Econometrics
arXiv:2410.23785 · PDF · DOI · OpenAlex · Extracted main text
Models with Conditional Moment Restrictions (CMRs) are popular in economics. These models involve finite and infinite dimensional parameters. The infinite dimensional components include conditional expectations, conditional choice probabilities, or policy functions, which might be flexibly estimated using Machine Learning tools. This paper presents a characterization of locally debiased moments for regular models defined by general semiparametric CMRs with possibly different conditioning variables. These moments are appealing as they are known to be less affected by first-step bias. Additionally, we study their existence and relevance. Such results apply to a broad class of smooth functionals of finite and infinite dimensional parameters that do not necessarily appear in the CMRs. As a leading application of our theory, we characterize debiased machine learning for settings of treatment effects with endogeneity, giving necessary and sufficient conditions. We present a large class of relevant debiased moments in this context. We then propose the Compliance Machine Learning Estimator (CML), based on a practically convenient orthogonal relevant moment. We show that the resulting estimand can be written as a convex combination of conditional local average treatment effects (LATE). Altogether, CML enjoys three appealing properties in the LATE framework: (1) local robustness to first-stage estimation, (2) an estimand that can be identified under a minimal relevance condition, and (3) a meaningful causal interpretation. Our numerical experimentation shows satisfactory relative performance of such an estimator. Finally, we revisit the Oregon Health Insurance Experiment, analyzed by Finkelstein et al. (2012). We find that the use of machine learning and CML suggest larger positive effects on health care utilization than previously determined.
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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 | Finkelstein, Amy, Sarah Taubman, Bill Wright, Mira Bernstein, Jonath… (2012) The Oregon health insurance experiment: evidence from the first year | 1.000 | 20 | 3 | 100% |
| 2 | Angrist, Joshua D and Guido W Imbens (1995) Two-stage least squares estimation of average causal effects in models with variable treatment intensity | 1.000 | 18 | 3 | 100% |
| 3 | Kolesár, Michal (2013) Estimation in an instrumental variables model with treatment effect heterogeneity, Tech | 1.000 | 11 | 3 | 100% |
| 4 | Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 10 | 4 | 100% |
| 5 | Coussens, Stephen and Jann Spiess (2021) Improving inference from simple instruments through compliance estimation | 1.000 | 10 | 4 | 100% |
| 6 | Chen, Xiaohong and Andres Santos (2018) Overidentification in regular models | 1.000 | 6 | 4 | 100% |
| 7 | Soczyński, Tymon (2020) When should we (not) interpret linear iv estimands as late? | 0.874 | 11 | 2 | 100% |
| 8 | Abadie, Alberto, Jiaying Gu, and Shu Shen (2024) Instrumental variable estimation with first-stage heterogeneity | 0.874 | 5 | 2 | 100% |
| 9 | Imbens, Guido W. and Joshua D. Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.874 | 5 | 2 | 100% |
| 10 | Abadie, Alberto (2003) Semiparametric instrumental variable estimation of treatment response models | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 43 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Automatic Debiased Machine Learning of Structural Parameters with General Conditional Moments | 1.000 | 9 | 4 |