arXiv 22 Jan 2024 · Econometrics · 2 citations (OpenAlex)
arXiv:2401.12050 · PDF · DOI · OpenAlex · Extracted main text
Combining short-term experimental data with observational data enables credible long-term policy evaluation. The literature offers two key but non-nested assumptions, namely the latent unconfoundedness (LU; Athey et al., 2020) and equi-confounding bias (ECB; Ghassami et al., 2022) conditions, to correct observational selection. Committing to the wrong assumption leads to biased estimation. To mitigate such risks, we provide a novel bracketing relationship (cf. Angrist and Pischke, 2009) repurposed for the setting with data combination: the LU-based estimand and the ECB-based estimand serve as the lower and upper bounds, respectively, with the true causal effect lying in between if either assumption holds. For researchers further seeking point estimates, our Lalonde-style exercise suggests the conservatively more robust LU-based lower bounds align closely with the hold-out experimental estimates for educational policy evaluation. We investigate the economic substantives of these findings through the lens of a nonparametric class of selection mechanisms and sensitivity analysis. We uncover as key the sub-martingale property and sufficient-statistics role (Chetty, 2009) of the potential outcomes of student test scores (Chetty et al., 2011, 2014).
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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 | Ghanem, D., Sant'Anna, P. H., and Wüthrich, K (2022) Selection and parallel trends | 1.000 | 13 | 5 | 100% |
| 2 | Ding, P. and Li, F (2019) A bracketing relationship between difference-in-differences and lagged-dependent-variable adjustment | 1.000 | 6 | 3 | 100% |
| 3 | Angrist, J. D. and Pischke, J.-S (2009) Mostly Harmless Econometrics: An Empiricist's Companion | 0.980 | 17 | 6 | 94% |
| 4 | LaLonde, R. J (1986) Evaluating the econometric evaluations of training programs with experimental data | 0.977 | 15 | 7 | 93% |
| 5 | Chetty, R (2009) Sufficient statistics for welfare analysis: A bridge between structural and reduced-form methods | 0.928 | 5 | 4 | 80% |
| 6 | Chetty, R., Friedman, J. N., Hilger, N., Saez, E., Schanzenbach, D.… (2011) How does your kindergarten classroom affect your earnings? evidence from project star | 0.928 | 4 | 4 | 100% |
| 7 | Chetty, R., Friedman, J. N., and Rockoff, J. E (2014) Measuring the impacts of teachers i: Evaluating bias in teacher value-added estimates | 0.928 | 4 | 4 | 100% |
| 8 | Ghassami, A., Shpitser, I., and Tchetgen, E. T (2022) Combining experimental and observational data for identification of long-term causal effects | 0.902 | 15 | 7 | 73% |
| 9 | Ashenfelter, O. and Card, D (1985) Using the longitudinal structure of earnings to estimate the effect of training programs | 0.897 | 18 | 3 | 72% |
| 10 | Roy, A. D (1951) Some thoughts on the distribution of earnings | 0.888 | 10 | 3 | 70% |
Showing the top 10 of 60 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Identification of Long-Term Treatment Effects via Temporal Links, Observational, and Experimental Data | 0.928 | 4 | 3 |
| 2 | The Experimental Selection Correction Estimator: Using Experiments to Remove Biases in Observational Estimates | 0.405 | 1 | 1 |