arXiv 31 Aug 2020 · Econometrics · 10 citations (OpenAlex)
arXiv:2008.13651 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a general causal inference method for treatment effects models with noisily measured confounders. The key feature is that a large set of noisy measurements are linked with the underlying latent confounders through an unknown, possibly nonlinear factor structure. The main building block is a local principal subspace approximation procedure that combines $K$-nearest neighbors matching and principal component analysis. Estimators of many causal parameters, including average treatment effects and counterfactual distributions, are constructed based on doubly-robust score functions. Large-sample properties of these estimators are established, which only require relatively mild conditions on the principal subspace approximation. The results are illustrated with an empirical application studying the effect of political connections on stock returns of financial firms, and a Monte Carlo experiment. The main technical and methodological results regarding the general local principal subspace approximation method may be of independent interest.
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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 | Acemoglu, Johnson, Kermani, Kwak, and Mitton (2016) The Value of Connections in Turbulent Times: Evidence from the United States | 1.000 | 9 | 3 | 100% |
| 2 | Bai (2009) Panel Data Models with Interactive Fixed Effects | 0.843 | 3 | 3 | 100% |
| 3 | Bai (2003) Inferential Theory for Factor Models of Large Dimensions | 0.737 | 3 | 2 | 100% |
| 4 | Abadie and Imbens (2006) Large Sample Properties of Matching Estimators for Average Treatment Effects | 0.644 | 2 | 2 | 100% |
| 5 | Abadie (2021) Using synthetic controls: Feasibility, data requirements, and methodological aspects | 0.644 | 2 | 2 | 100% |
| 6 | Ahn and Horenstein (2013) Eigenvalue Ratio Test for the Number of Factors | 0.644 | 2 | 2 | 100% |
| 7 | Bai and Ng (2002) Determining the Number of Factors in Approximate Factor Models | 0.644 | 2 | 2 | 100% |
| 8 | Bonhomme, Lamadon, and Manresa (2021) Discretizing Unobserved Heterogeneity | 0.644 | 2 | 2 | 100% |
| 9 | Hu and Schennach (2008) Instrumental Variable Treatment of Nonclassical Measurement Error Models | 0.644 | 2 | 2 | 100% |
| 10 | Wang and Fan (2017) Asymptotics of Empirical Eigenstructure for High Dimensional Spiked Covariance | 0.644 | 2 | 2 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.