arXiv 29 Mar 2022 · Econometrics · 1 citations (OpenAlex)
arXiv:2203.15890 · PDF · DOI · OpenAlex · Extracted main text
This study demonstrates the existence of a testable condition for the identification of the causal effect of a treatment on an outcome in observational data, which relies on two sets of variables: observed covariates to be controlled for and a suspected instrument. Under a causal structure commonly found in empirical applications, the testable conditional independence of the suspected instrument and the outcome given the treatment and the covariates has two implications. First, the instrument is valid, i.e. it does not directly affect the outcome (other than through the treatment) and is unconfounded conditional on the covariates. Second, the treatment is unconfounded conditional on the covariates such that the treatment effect is identified. We suggest tests of this conditional independence based on machine learning methods that account for covariates in a data-driven way and investigate their asymptotic behavior and finite sample performance in a simulation study. We also apply our testing approach to evaluating the impact of fertility on female labor supply when using the sibling sex ratio of the first two children as supposed instrument, which by and large points to a violation of our testable implication for the moderate set of socio-economic covariates considered.
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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 | Angrist, J., Evans, W (1998) Children and their parents labor supply: Evidence from exogeneous variation in family size | 0.874 | 5 | 2 | 100% |
| 2 | Pearl, J (2000) Causality: Models, Reasoning, and Inference | 0.843 | 5 | 3 | 60% |
| 3 | Racine, J (1997) Consistent significance testing for nonparametric regression | 0.843 | 3 | 3 | 100% |
| 4 | Racine, J.S., Hart, J., Li, Q (2006) Testing the significance of categorical predictor variables in nonparametric regression models | 0.843 | 3 | 3 | 100% |
| 5 | Pearl, J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference | 0.737 | 4 | 3 | 50% |
| 6 | Caetano, C., Caetano, G., Fe, H., Nielsen, E (2021) A dummy test of identi?cation in models with bunching | 0.737 | 3 | 2 | 100% |
| 7 | Angrist, J.D., Rokkanen, M (2015) Wanna get away? regression discontinuity estimation of exam school effects away from the cutoff | 0.737 | 3 | 2 | 100% |
| 8 | de Luna, X., Johansson, P (2014) Testing for the unconfoundedness assumption using an instrumental assumption | 0.737 | 3 | 2 | 100% |
| 9 | Wooldridge, J.M (1992) A test for functional form against nonparametric alternatives | 0.737 | 3 | 2 | 100% |
| 10 | Athey, S., Imbens, G (2016) Recursive partitioning for heterogeneous causal effects | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 71 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2406.13826 | 0.941 | 12 | 5 |
| 2 | Learning and Testing Exposure Mappings of Interference using Graph Convolutional Autoencoder | 0.909 | 8 | 3 |
| 3 | 2603.04109 | 0.843 | 4 | 4 |
| 4 | 2407.08602 | 0.511 | 2 | 1 |
| 5 | A joint test of unconfoundedness and common trends | 0.405 | 1 | 1 |