Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects
نویسندگان
چکیده
منابع مشابه
Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects
This paper develops a semi-parametric Bayesian regression model for estimating heterogeneous treatment effects from observational data. Standard nonlinear regression models, which may work quite well for prediction, can yield badly biased estimates of treatment effects when fit to data with strong confounding. Our Bayesian causal forests model avoids this problem by directly incorporating an es...
متن کاملConfounding Equivalence in Causal Inference
The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same bias-reducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e. satisfy the back-door criterion) or (2) the Markov boundaries surrounding the treatment variable are identical in both sets. We...
متن کاملBayesian Inference for Geostatistical Regression Models
The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. Often, these standard criteria suggest models that are too complex in an effort to compensate for spatial correlation ignored in the se...
متن کاملSemiparametric Bayesian inference for regression models
This paper presents a method for Bayesian inference for the regression parameters in a linear model with independent and identically distributed errors that does not require the specification of a parametric family of densities for the error distribution. This method first selects a nonparametric kernel density estimate of the error distribution which is unimodal and based on the least-squares ...
متن کاملBayesian Multimodel Inference for Geostatistical Regression Models
The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC) method is investigated for the calculation of parameter estimates and posterior model probabilities f...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2017
ISSN: 1556-5068
DOI: 10.2139/ssrn.3048177