Songnian Chen, Shakeeb Khan, Xun Tang
arXiv 9 Aug 2022 · Econometrics · publishedJournal of Econometrics (2023)
arXiv:2208.05047 · PDF · DOI · OpenAlex · Extracted main text
We identify and estimate treatment effects when potential outcomes are weakly separable with a binary endogenous treatment. Vytlacil and Yildiz (2007) proposed an identification strategy that exploits the mean of observed outcomes, but their approach requires a monotonicity condition. In comparison, we exploit full information in the entire outcome distribution, instead of just its mean. As a result, our method does not require monotonicity and is also applicable to general settings with multiple indices. We provide examples where our approach can identify treatment effect parameters of interest whereas existing methods would fail. These include models where potential outcomes depend on multiple unobserved disturbance terms, such as a Roy model, a multinomial choice model, as well as a model with endogenous random coefficients. We establish consistency and asymptotic normality of our estimators.
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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 | Vytlacil and Yildiz (2007) Dummy Endogenous Variables in Weakly Separable Models | 1.000 | 25 | 7 | 100% |
| 2 | Heckman and Vytlacil (2005) Structural Equations, Treatment Effects, and Econometric Policy Evaluation | 0.737 | 3 | 2 | 100% |
| 3 | Carneiro and Lee (2009) Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enro… | 0.737 | 3 | 2 | 100% |
| 4 | Heckman and Vytlacil (2007) Econometric evaluation of social programs | 0.644 | 2 | 2 | 100% |
| 5 | Shaikh and Vytlacil (2011) Partial Identification in Triangular Systems of Equations with Binary Dependent Variables | 0.644 | 2 | 2 | 100% |
| 6 | Vuong and Xu (2017) Counterfactual mapping and individual treatment effects in nonseparable models with binary endogeneity | 0.644 | 2 | 2 | 100% |
| 7 | Abrevaya and Xu (2022) Estimation of treatment effects under endogenous heteroskedasticity | 0.511 | 2 | 1 | 100% |
| 8 | Ahn, Powell, Ichimura, and Ruud (2017) Simple Estimators for Invertible Index Models | 0.511 | 2 | 1 | 100% |
| 9 | Mogstad, Santos, and Torgovitsky (2018) Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters | 0.511 | 2 | 1 | 100% |
| 10 | Carneiro, Vytlacil, and Heckman (2010) Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 35 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inference on High Dimensional Selective Labeling Models | 0.644 | 2 | 2 |