Open AccessPosted Content
Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects
TLDR
This article proposed a Bayesian causal forest model for estimating heterogeneous treatment effects from observational data, which is geared specifically towards situations with small effect sizes, heterogeneous effects, and strong confounding.Abstract:
This paper presents a novel nonlinear regression model for estimating heterogeneous treatment effects from observational data, geared specifically towards situations with small effect sizes, heterogeneous effects, and strong confounding. Standard nonlinear regression models, which may work quite well for prediction, have two notable weaknesses when used to estimate heterogeneous treatment effects. First, they can yield badly biased estimates of treatment effects when fit to data with strong confounding. The Bayesian causal forest model presented in this paper avoids this problem by directly incorporating an estimate of the propensity function in the specification of the response model, implicitly inducing a covariate-dependent prior on the regression function. Second, standard approaches to response surface modeling do not provide adequate control over the strength of regularization over effect heterogeneity. The Bayesian causal forest model permits treatment effect heterogeneity to be regularized separately from the prognostic effect of control variables, making it possible to informatively "shrink to homogeneity". We illustrate these benefits via the reanalysis of an observational study assessing the causal effects of smoking on medical expenditures as well as extensive simulation studies.read more
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Proceedings Article
What is Causal Inference
Abstract: This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in (Pearl, 2000a). The theory unifies the graphical, potential-outcome (Neyman-Rubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper establishes a methodology for inferring (from a combination of data and assumptions) the answers to three types of causal queries: (1) queries about the effect of potential interventions, (2) queries about counterfactuals, and (3) queries about the direct (or indirect) effect of one event on another.
Journal ArticleDOI
A Survey of Learning Causality with Data: Problems and Methods
TL;DR: In this paper, a comprehensive and structured review of both traditional and frontier methods in learning causality and relations along with the connections between causal effects and machine learning is presented. But, the authors point out on a case-by-case basis how big data facilitates, complicates, or motivates each approach.
Posted Content
Estimating Treatment Effects with Causal Forests: An Application
Susan Athey,Stefan Wager +1 more
TL;DR: The authors apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges, and discuss how causal forests use estimated propensity scores to be more robust to confounding and how they handle data with clustered errors.
Posted Content
A Survey on Causal Inference
TL;DR: This survey provides a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks, and presents the plausible applications of these methods, including the applications in advertising, recommendation, medicine, and so on.
Journal ArticleDOI
Causal inference and counterfactual prediction in machine learning for actionable healthcare
Mattia Prosperi,Yi Guo,Yi Guo,Matthew Sperrin,James S. Koopman,Jae Min,Xing He,Shannan N Rich,Mo Wang,Iain Buchan,Jian-Guo Bian,Jian-Guo Bian +11 more
TL;DR: How target trials, transportability, and prediction invariance are linchpins to developing and testing intervention models and a true causal model is contained in the set of all prediction models whose accuracy does not vary across different settings is discussed.
References
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Journal ArticleDOI
The central role of the propensity score in observational studies for causal effects
TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
Book ChapterDOI
Domain-adversarial training of neural networks
Yaroslav Ganin,Evgeniya Ustinova,Hana Ajakan,Pascal Germain,Hugo Larochelle,François Laviolette,Mario Marchand,Victor Lempitsky +7 more
TL;DR: In this article, a new representation learning approach for domain adaptation is proposed, in which data at training and test time come from similar but different distributions, and features that cannot discriminate between the training (source) and test (target) domains are used to promote the emergence of features that are discriminative for the main learning task on the source domain.
Journal ArticleDOI
Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
TL;DR: In this paper, a folded-noncentral-$t$ family of conditionally conjugate priors for hierarchical standard deviation parameters is proposed, and weakly informative priors in this family are considered.
Journal ArticleDOI
Doubly robust estimation in missing data and causal inference models
Heejung Bang,James M. Robins +1 more
TL;DR: The results of simulation studies are presented which demonstrate that the finite sample performance of DR estimators is as impressive as theory would predict and the proposed method is applied to a cardiovascular clinical trial.
Journal ArticleDOI
BART: Bayesian additive regression trees
TL;DR: A Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior.
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