Bayesian Inference for the Causal Effect of Mediation

Bayesian inference for the causal effect of mediation.

We propose a nonparametric Bayesian approach to estimate the natural direct and indirect effects through a mediator in the setting of a continuous mediator and a binary response. Several conditional independence assumptions are introduced (with corresponding sensitivity parameters) to make these effects identifiable from the observed data. We suggest strategies for eliciting sensitivity paramet...

Bayesian Matching for Causal Inference

In this paper we provide Bayesian matching methods for finding the causal effect of a binary intake variable x ∈ {0, 1} on an outcome of interest y. One technique we introduce is a Bayesian variant of the classic Rosenbaum and Rubin (1983, 1984) propensity score matching method. We show how it is possible to find the posterior distribution of the Bayesian matched sample average treatment effect...

“the effect of risk aversion on the demand for life insurance: the case of iranian life insurance market”

abstract: about 60% of total premium of insurance industry is pertained?to life policies in the world; while the life insurance total premium in iran is less than 6% of total premium in insurance industry in 2008 (sigma, no 3/2009). among the reasons that discourage the life insurance industry is the problem of adverse selection. adverse selection theory describes a situation where the inf...

Bayesian Nonparametric Modeling for Causal Inference

Researchers have long struggled to identify causal effects in non-experimental settings. Many recently-proposed strategies assume ignorability of the treatment assignment mechanism and require fitting two models – one for the assignment mechanism and one for the response surface. We propose a strategy that instead focuses on very flexibly modeling just the response surface using a Bayesian nonp...

Explanation in causal inference: Methods for mediation and interaction.