arXiv 27 Mar 2024 · Econometrics
arXiv:2403.18503 · PDF · DOI · OpenAlex · Extracted main text
Treatment effect heterogeneity is of a great concern when evaluating policy impact: "is the treatment Pareto-improving?", "what is the proportion of people who are better off under the treatment?", etc. However, even in the simple case of a binary random treatment, existing analysis has been mostly limited to an average treatment effect or a quantile treatment effect, due to the fundamental limitation that we cannot simultaneously observe both treated potential outcome and untreated potential outcome for a given unit. This paper assumes a conditional independence assumption that the two potential outcomes are independent of each other given a scalar latent variable. With a specific example of strictly increasing conditional expectation, I label the latent variable as 'latent rank' and motivate the identifying assumption as 'latent rank invariance.' In implementation, I assume a finite support on the latent variable and propose an estimation strategy based on a nonnegative matrix factorization. A limiting distribution is derived for the distributional treatment effect estimator, using Neyman orthogonality.
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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 | Jones, Molitor and Reif (2019) What do workplace wellness programs do? Evidence from the Illinois workplace wellness study | 1.000 | 13 | 4 | 100% |
| 2 | Hu and Schennach (2008) Instrumental variable treatment of nonclassical measurement error models | 0.794 | 10 | 4 | 50% |
| 3 | Carneiro, Hansen and Heckman (2003) 2001 Lawrence R. Klein Lecture Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and… | 0.737 | 3 | 2 | 100% |
| 4 | Henry, Kitamura and Salanié (2014) Partial identification of finite mixtures in econometric models | 0.737 | 3 | 2 | 100% |
| 5 | Cunha, Heckman and Schennach (2010) Estimating the technology of cognitive and noncognitive skill formation | 0.693 | 5 | 1 | 100% |
| 6 | Fan and Park (2010) Sharp bounds on the distribution of treatment effects and their statistical inference | 0.644 | 2 | 2 | 100% |
| 7 | Hu (2008) Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution | 0.511 | 2 | 2 | 50% |
| 8 | Deaner (2023) Proxy controls and panel data | 0.511 | 2 | 1 | 100% |
| 9 | Kedagni (2023) Identifying treatment effects in the presence of confounded types | 0.511 | 2 | 1 | 100% |
| 10 | Miao, Geng and Tchetgen Tchetgen (2018) Identifying causal effects with proxy variables of an unmeasured confounder | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 37 scored citations.