Nicolas Apfel, Julia Hatamyar, Martin Huber, Jannis Kueck
arXiv 5 Jul 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2407.04448 · PDF · DOI · OpenAlex · Extracted main text
This study introduces a data-driven, machine learning-based method to detect suitable control variables and instruments for assessing the causal effect of a treatment on an outcome in observational data, if they exist. Our approach tests the joint existence of instruments, which are associated with the treatment but not directly with the outcome (at least conditional on observables), and suitable control variables, conditional on which the treatment is exogenous, and learns the partition of instruments and control variables from the observed data. The detection of sets of instruments and control variables relies on the condition that proper instruments are conditionally independent of the outcome given the treatment and suitable control variables. We establish the consistency of our method for detecting control variables and instruments under certain regularity conditions, investigate the finite sample performance through a simulation study, and provide an empirical application to labor market data from the Job Corps study.
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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 | Huber, M., Kueck, J (2022) Testing the identification of causal effects in data self | 0.977 | 15 | 4 | 93% |
| 2 | Guo, Z., Kang, H., Cai, T.T., Small, D.S (2018) Confidence Intervals for Causal Effects with Invalid Instruments by Using Two-Stage Hard Thresholding with Voting | 0.811 | 4 | 2 | 100% |
| 3 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.794 | 8 | 5 | 50% |
| 4 | Black, D.A., Joo, J., LaLonde, R.J., Smith, J.A., Taylor, E.J (2015) Simple tests for selection bias: Learning more from instrumental variables | 0.737 | 3 | 2 | 100% |
| 5 | Windmeijer, F., Liang, X., Hartwig, F.P., Bowden, J (2021) The confidence interval method for selecting valid instrumental variables | 0.737 | 3 | 2 | 100% |
| 6 | de Luna, X., Johansson, P (2014) Testing for the unconfoundedness assumption using an instrumental assumption | 0.737 | 3 | 2 | 100% |
| 7 | Finkelstein, A., Taubman, S., Wright, B., Bernstein, M., Gruber, J.,… (2012) The oregon health insurance experiment: evidence from the first year | 0.737 | 3 | 2 | 100% |
| 8 | Soleymani, A., Raj, A., Bauer, S., Schölkopf, B., Besserve, M (2022) Causal feature selection via orthogonal search | 0.644 | 2 | 2 | 100% |
| 9 | Hassanpour, N., Greiner, R (2019) Learning disentangled representations for counterfactual regression | 0.511 | 2 | 1 | 100% |
| 10 | Hong, Y., White, H (1995) Consistent specification testing via nonparametric series regression | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 61 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Testing Effect Homogeneity and Confounding in High-Dimensional Experimental and Observational Studies | 1.000 | 9 | 5 |
| 2 | 2603.04109 | 0.874 | 6 | 2 |
| 3 | Learning and Testing Exposure Mappings of Interference using Graph Convolutional Autoencoder | 0.737 | 5 | 2 |
| 4 | 2407.08602 | 0.405 | 1 | 1 |