Instrument Validity Tests With Causal Forests
Bibliographic Data
| ID | 19418633 |
|---|---|
| Authors | Helmut Farbmacher (0000-0002-3789-7742, Max Planck Society, corresponding author), Raphael Guber (Max Planck Society), Sven Klaassen (Universität Hamburg) |
| Year | 2022 |
| Volume | 40 |
| Issue | 2 |
| Pages | 605-614 |
| Publication date | 2022-04-03 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2020.1847122 |
| OpenAlex | W3114718161 |
| Language | EN |
| References cited | 53 |
Assumptions that are sufficient to identify local average treatment effects (LATEs) generate necessary conditions that allow instrument validity to be refuted. The degree to which instrument validity is violated, however, probably varies across subpopulations. In this article, we use causal forests to search and test for such local violations of the LATE assumptions in a data-driven way. Unlike previous instrument validity tests, our procedure is able to detect local violations. We evaluate the performance of our procedure in simulations and apply it in two different settings: parental preferences for mixed-sex composition of children and the Vietnam draft lottery
Econometrics · External validity · Instrumental variable · Internal validity · Lottery · Statistics · Advanced Causal Inference Techniques · Computer Science · Mathematics · Psychology · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Ecology
Structural Equations, Treatment Effects, and Econometric Policy Evaluation1
Semiparametric instrumental variable estimation of treatment response models
Natural “Natural Experiments” in Economics
Recursive partitioning for heterogeneous causal effects
Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Double/debiased machine learning for treatment and structural parameters
Inference on Treatment Effects after Selection among High-Dimensional Controls
Generalized random forests
The Oregon Health Insurance Experiment
Estimation of Regression Coefficients When Some Regressors are not Always Observed
Identification of Causal Effects Using Instrumental Variables
Random Forests
Null
Identification and Estimation of Local Average Treatment Effects
Regression Shrinkage and Selection Via the Lasso
Direct and Indirect Effects Based on Difference-in-Differences With an Application to Political Preferences Following the Vietnam Draft Lottery
Testing Instrument Validity for Late Identification Based on Inequality Moment Constraints
Testing Local Average Treatment Effect Assumptions
Life-Cycle Labor Supply and Fertility
High-Dimensional Methods and Inference on Structural and Treatment Effects
| Citation velocity | historical |
|---|---|
| Highly cited | No |