Susan Athey, Raj Chetty, Guido Imbens
arXiv 17 Jun 2020 · Statistics — Methodology · 25 citations (OpenAlex)
arXiv:2006.09676 · PDF · DOI · OpenAlex · Extracted main text
Researchers increasingly have access to two types of data: (i) large observational datasets where treatment (e.g., class size) is not randomized but several primary outcomes (e.g., graduation rates) and secondary outcomes (e.g., test scores) are observed and (ii) experimental data in which treatment is randomized but only secondary outcomes are observed. We develop a new method to estimate treatment effects on primary outcomes in such settings. We use the difference between the secondary outcome and its predicted value based on the experimental treatment effect to measure selection bias in the observational data. Controlling for this estimate of selection bias yields an unbiased estimate of the treatment effect on the primary outcome under a new assumption that we term latent unconfoundedness, which requires that the same confounders affect the primary and secondary outcomes. Latent unconfoundedness weakens the assumptions underlying commonly used surrogate estimators. We apply our estimator to identify the effect of third grade class size on students outcomes. Estimated impacts on test scores using OLS regressions in observational school district data have the opposite sign of estimates from the Tennessee STAR experiment. In contrast, selection-corrected estimates in the observational data replicate the experimental estimates. Our estimator reveals that reducing class sizes by 25% increases high school graduation rates by 0.7 percentage points. Controlling for observables does not change the OLS estimates, demonstrating that experimental selection correction can remove biases that cannot be addressed with standard controls.
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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 | V Joseph Hotz, Guido W Imbens, and Julie H Mortimer (2005) Predicting the efficacy of future training programs using past experiences at other locations self | 0.843 | 3 | 3 | 100% |
| 2 | Guido W Imbens and Whitney K Newey (2009) Identification and estimation of triangular simultaneous equations models without additivity self | 0.811 | 4 | 2 | 100% |
| 3 | Susan Athey and Guido W Imbens (2006) Identification and inference in nonlinear difference-in-differences models self | 0.737 | 3 | 2 | 100% |
| 4 | Raj Chetty, John N Friedman, Nathaniel Hilger, Emmanuel Saez, Diane… (2011) How does your kindergarten classroom affect your earnings? evidence from project star self | 0.737 | 3 | 2 | 100% |
| 5 | Raj Chetty, John N Friedman, and Jonah E Rockoff (2014) Measuring the impacts of teachers i: Evaluating bias in teacher value-added estimates self | 0.737 | 3 | 2 | 100% |
| 6 | Nathan Kallus and Xiaojie Mao (2020) On the role of surrogates in the efficient estimation of treatment effects with limited outcome data | 0.737 | 3 | 2 | 100% |
| 7 | Alan B Krueger (1999) Experimental estimates of education production functions | 0.737 | 3 | 2 | 100% |
| 8 | Evan Rosenman, Art B Owen, Michael Baiocchi, and Hailey Banack (2018) Propensity score methods for merging observational and experimental datasets | 0.737 | 3 | 2 | 100% |
| 9 | Susan Athey, Raj Chetty, Guido W Imbens, and Hyunseung Kang (2019) The surrogate index: Combining short-term proxies to estimate long-term treatment effects more rapidly and precisely self | 0.644 | 2 | 2 | 100% |
| 10 | James J Heckman (1979) Sample selection bias as a specification error | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 62 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 | 0.950 | 7 | 5 |
| 2 | Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference | 0.405 | 1 | 1 |