Bayesian Versus Maximum Likelihood Estimation of Treatment Effects in Bivariate Probit Instrumental Variable Models

Florian M. Hollenbach, Jacob M. Montgomery, Adriana Crespo-Tenorio

Research output: Contribution to journalJournal articleResearchpeer-review


Bivariate probit models are a common choice for scholars wishing to estimate causal effects in instrumental variable models where both the treatment and outcome are binary. However, standard maximum likelihood approaches for estimating bivariate probit models are problematic. Numerical routines in popular software suites frequently generate inaccurate parameter estimates and even estimated correctly, maximum likelihood routines provide no straightforward way to produce estimates of uncertainty for causal quantities of interest. In this note, we show that adopting a Bayesian approach provides more accurate estimates of key parameters and facilitates the direct calculation of causal quantities along with their attendant measures of uncertainty.
Original languageEnglish
JournalPolitical Science Research and Methods
Issue number3
Pages (from-to)651-659
Number of pages9
Publication statusPublished - Jul 2019
Externally publishedYes

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