Causal inference with Bayes rule
The concept of causality has a controversial history. The question of whether it is possible to represent and address causal problems with probability theory, or if fundamentally new mathematics such as the do-calculus is required has been hotly debated, In this paper we demonstrate that, while it is critical to explicitly model our assumptions on the impact of intervening in a system, provided we do so, estimating causal effects can be done entirely within the standard Bayesian paradigm. The invariance assumptions underlying causal graphical models can be encoded in ordinary Probabilistic graphical models, allowing causal estimation with Bayesian statistics, equivalent to the do-calculus.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal InferenceSimilar Papers 제목 키워드 기반
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect
A key question in causal inference analyses is how to find subgroups with elevated treatment effects. This paper takes a machine learning approach and introduces a generative model, Causal Rule Sets (CRS), for interpreta…
Causal InferenceSubgroup DiscoveryThe Compositional Structure of Bayesian Inference
Bayes' rule tells us how to invert a causal process in order to update our beliefs in light of new evidence. If the process is believed to have a complex compositional structure, we may observe that the inversion of the …
Bayesian InferenceCausal Inference through a Witness Protection Program
One of the most fundamental problems in causal inference is the estimation of a causal effect when variables are confounded. This is difficult in an observational study, because one has no direct evidence that all confou…
Bayesian InferenceCausal InferenceHybrid Local Causal Discovery
Local causal discovery aims to learn and distinguish the direct causes and effects of a target variable from observed data. Existing constraint-based local causal discovery methods use AND or OR rules in constructing the…
Causal DiscoveryA Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric Approaches
Substantial advances in Bayesian methods for causal inference have been developed in recent years. We provide an introduction to Bayesian inference for causal effects for practicing statisticians who have some familiarit…
Bayesian InferenceCausal Inference