Analysis of treatment response data without the joint distribution of potential outcomes

DOI10.1016/j.jeconom.2006.07.009zbMath1247.91132OpenAlexW1984059321MaRDI QIDQ451244

Siddhartha Chib

Publication date: 23 September 2012

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2006.07.009

zbMATH Keywords

instrumental variableMarkov chain Monte Carlomarginal likelihoodconfoundingstructural modelpredictive treatment effect

Mathematics Subject Classification ID

Statistical methods; economic indices and measures (91B82)

Related Items

Bayesian treatment effects models with variable selection for panel outcomes with an application to earnings effects of maternity leave, Analysis of treatment response data from eligibility designs, Efficient Gibbs sampler for Bayesian analysis of a sample selection model, Treatment Effects: A Bayesian Perspective, Semiparametric Bayesian multiple imputation for regression models with missing mixed continuous-discrete covariates, The role of exchangeability in causal inference, Estimation of sample selection models with two selection mechanisms, Semiparametric Bayesian Estimation for Marginal Parametric Potential Outcome Modeling: Application to Causal Inference, Estimating a treatment effect under uncertainty with application to a high‐speed railway system, Bayesian inference in a correlated random coefficients model: modeling causal effect heterogeneity with an application to heterogeneous returns to schooling

Cites Work

• Unnamed Item
• Learning about the across-regime correlation in switching regression models
• Bayesian analysis of cross-section and clustered data treatment models
• Measuring the unidentified parameter of the extended Roy model of selectivity
• Semiparametric Bayes analysis of longitudinal data treatment models
• Markov chains for exploring posterior distributions. (With discussion)
• Marginal Likelihood from the Gibbs Output
• Unionism and Wage Rates: A Simultaneous Equations Model with Qualitative and Limited Dependent Variables
• Identification and Estimation of Local Average Treatment Effects
• Causal Inference Without Counterfactuals
• Bayesian Analysis of Binary and Polychotomous Response Data