Dmitry Arkhangelsky, Guido Imbens
arXiv 5 Jul 2018 · Econometrics · publishedThe Review of Economic Studies (2023) · 19 citations (OpenAlex)
arXiv:1807.02099 · PDF · DOI · OpenAlex · Extracted main text
We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity. We consider a general model with group-level unconfoundedness and provide conditions under which aggregate balancing statistics -- group-level averages of functions of treatments and covariates -- are sufficient to eliminate differences between groups. Building on these results, we reinterpret commonly used linear fixed-effect regression estimators by writing them in the Mundlak form as linear regression estimators without fixed effects but including group averages. We use this representation to develop Generalized Mundlak Estimators (GMEs) that capture group differences through group averages of (functions of) the unit-level variables and adjust for these group differences in flexible and robust ways in the spirit of the modern causal literature.
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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 | Guido W Imbens and Donald B Rubin (2015) Causal Inference in Statistics, Social, and Biomedical Sciences self | 1.000 | 5 | 3 | 100% |
| 2 | Joseph G Altonji and Rosa L Matzkin (2005) Cross section and panel data estimators for nonseparable models with endogenous regressors | 0.843 | 3 | 3 | 100% |
| 3 | Susan Athey, Guido W Imbens, and Stefan Wager (2018) Approximate residual balancing: debiased inference of average treatment effects in high dimensions self | 0.843 | 3 | 3 | 100% |
| 4 | James M Robins and Andrea Rotnitzky (1995) Semiparametric efficiency in multivariate regression models with missing data | 0.843 | 3 | 3 | 100% |
| 5 | Paul R Rosenbaum and Donald B Rubin (1983) The central role of the propensity score in observational studies for causal effects | 0.843 | 3 | 3 | 100% |
| 6 | Yair Mundlak (1978) On the pooling of time series and cross section data | 0.811 | 4 | 2 | 100% |
| 7 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 3 | 2 | 100% |
| 8 | Tony Lancaster (2000) The incidental parameter problem since 1948 | 0.737 | 3 | 2 | 100% |
| 9 | Jerzy Neyman and Elizabeth L Scott (1948) Consistent estimates based on partially consistent observations | 0.737 | 3 | 2 | 100% |
| 10 | Gary Chamberlain (1984) Panel data | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 54 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.