Zhewen Pan, Zhengxin Wang, Junsen Zhang, Yahong Zhou
arXiv 31 Jan 2024 · Econometrics
arXiv:2401.17595 · PDF · DOI · OpenAlex · Extracted main text
We propose a method for defining, identifying, and estimating the marginal treatment effect (MTE) without imposing the instrumental variable (IV) assumptions of independence, exclusion, and separability (or monotonicity). Under a new definition of the MTE based on reduced-form treatment error that is statistically independent of the covariates, we find that the relationship between the MTE and standard treatment parameters holds in the absence of IVs. We provide a set of sufficient conditions ensuring the identification of the defined MTE in an environment of essential heterogeneity. The key conditions include a linear restriction on potential outcome regression functions, a nonlinear restriction on the propensity score, and a conditional mean independence restriction that will lead to additive separability. We prove this identification using the notion of semiparametric identification based on functional form. And we provide an empirical application for the Head Start program to illustrate the usefulness of the proposed method in analyzing heterogenous causal effects when IVs are elusive.
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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 | Escanciano, Juan Carlos and Jacho-Chávez, David and Lewbel, Arthur (2016) Identification and estimation of semiparametric two-step models | 1.000 | 11 | 3 | 100% |
| 2 | Heckman, James J and Vytlacil, Edward (2005) Structural equations, treatment effects, and econometric policy evaluation | 1.000 | 6 | 3 | 100% |
| 3 | De Haan, Monique and Leuven, Edwin (2020) Head start and the distribution of long-term education and labor market outcomes | 0.874 | 5 | 2 | 100% |
| 4 | Ahn, Hyungtaik and Powell, James L (1993) Semiparametric estimation of censored selection models with a nonparametric selection mechanism | 0.843 | 3 | 3 | 100% |
| 5 | Pan, Zhewen and Zhou, Xianbo and Zhou, Yahong (2022) Semiparametric estimation of a censored regression model subject to nonparametric sample selection self | 0.843 | 3 | 3 | 100% |
| 6 | Powell, James L. and Stock, James H. and Stoker, Thomas M (1989) Semiparametric estimation of index coefficients | 0.843 | 3 | 3 | 100% |
| 7 | Brinch, Christian N and Mogstad, Magne and Wiswall, Matthew (2017) Beyond LATE with a discrete instrument | 0.737 | 3 | 2 | 100% |
| 8 | Heckman, James J and Vytlacil, Edward (2001) Local instrumental variables | 0.737 | 3 | 2 | 100% |
| 9 | Li, Qi and Racine, Jeffrey Scott (2007) Nonparametric Econometrics: Theory and Practice | 0.737 | 3 | 2 | 100% |
| 10 | Zhou, Xiang and Xie, Yu (2019) Marginal treatment effects from a propensity score perspective | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 76 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Locally robust semiparametric estimation of sample selection models without exclusion restrictions | 0.644 | 2 | 2 |
| 2 | Extrapolating LATE with Weak IVs | 0.511 | 2 | 2 |