Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions
Bibliographic Data
| ID | 19419934 |
|---|---|
| Authors | Sung Jae Jun (Department of Economics, Pennsylvania State University, University Park, PA, corresponding author), Sokbae Lee (0000-0003-4080-7733, Columbia University) |
| Year | 2024 |
| Volume | 42 |
| Issue | 3 |
| Pages | 998-1009 |
| Publication date | 2024-07-02 |
| Open Access | No |
| 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 | 10.1080/07350015.2023.2277164 |
| OpenAlex | W3188610826 |
| Language | EN |
| References cited | 42 |
We study causal inference under case-control and case-population sampling. Specifically, we focus on the binary-outcome and binary-treatment case, where the parameters of interest are causal relative and attributable risks defined via the potential outcome framework. It is shown that strong ignorability is not always as powerful as it is under random sampling and that certain monotonicity assumptions yield comparable results in terms of sharp identified intervals. Specifically, the usual odds ratio is shown to be a sharp identified upper bound on causal relative risk under the monotone treatment response and monotone treatment selection assumptions. We offer algorithms for inference on the causal parameters that are aggregated over the true population distribution of the covariates. We show the usefulness of our approach by studying three empirical examples: the benefit of attending private school for entering a prestigious university in Pakistan; the relationship between staying in school and getting involved with drug-trafficking gangs in Brazil; and the link between physicians’ hours and size of the group practice in the United States
Causal inference · Covariate · Econometrics · Inference · Logistic regression · Mathematical economics · Monotone polygon · Monotonic function · Odds · Population · Statistics · Advanced Causal Inference Techniques · Computer Science · Mathematics · Medicine · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Artificial Intelligence
Causality
Monotone Instrumental Variables
The Estimation of Choice Probabilities from Choice Based Samples
On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
Identifying the Effects of SNAP (Food Stamps) on Child Health Outcomes When Participation Is Endogenous and Misreported
Identification Problems in the Social Sciences
University Choice
Reevaluating the Middle-Class Protest Paradigm
| Highly cited | No |
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