313 citations · 323 across the 4 of their papers we have counts for
6 papers
Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho
Liangyuan Hu
Hahn et al. (2020) offers an extensive study to explicate and evaluate the performance of the BCF model in different settings and provides a detailed discussion about its utility i…
Do forecasts of bankruptcy cause bankruptcy? A machine learning sensitivity analysis
Demetrios Papakostas, P. Richard Hahn, Jared Murray +2
It is widely speculated that auditors' public forecasts of bankruptcy are, at least in part, self-fulfilling prophecies in the sense that they might actually cause bankruptcies tha…
Estimating heterogeneous effects of continuous exposures using Bayesian tree ensembles: revisiting the impact of abortion rates on crime
Spencer Woody, Carlos M. Carvalho, P. Richard Hahn +1
In estimating the causal effect of a continuous exposure or treatment, it is important to control for all confounding factors. However, most existing methods require parametric spe…
Efficient sampling for Gaussian linear regression with arbitrary priors
P. Richard Hahn, Jingyu He, Hedibert Lopes
This paper develops a slice sampler for Bayesian linear regression models with arbitrary priors. The new sampler has two advantages over current approaches. One, it is faster than…
Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects
P. Richard Hahn, Jared S. Murray, Carlos Carvalho
This paper presents a novel nonlinear regression model for estimating heterogeneous treatment effects from observational data, geared specifically towards situations with small eff…
Variable Selection in Seemingly Unrelated Regressions with Random Predictors
David Puelz, P. Richard Hahn, Carlos Carvalho
This paper considers linear model selection when the response is vector-valued and the predictors are randomly observed. We propose a new approach that decouples statistical infere…