178 research outputs found
Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example
Local Constraint-Based Causal Discovery under Selection Bias
We consider the problem of discovering causal relations from independence constraints selection bias in addition to confounding is present. While the seminal FCI algorithm is sound and complete in this setup, no criterion for the causal interpretation of its output under selection bias is presently known. We focus instead on local patterns of independence relations, where we find no sound method for only three variable that can include background knowledge. Y-Structure patterns are shown to be sound in predicting causal relations from data under selection bias, where cycles may be present. We introduce a finite-sample scoring rule for Y-Structures that is shown to successfully predict causal relations in simulation experiments that include selection mechanisms. On real-world microarray data, we show that a Y-Structure variant performs well across different datasets, potentially circumventing spurious correlations due to selection bias
Joint Causal Inference from Multiple Contexts
The gold standard for discovering causal relations is by means of
experimentation. Over the last decades, alternative methods have been proposed
that can infer causal relations between variables from certain statistical
patterns in purely observational data. We introduce Joint Causal Inference
(JCI), a novel approach to causal discovery from multiple data sets from
different contexts that elegantly unifies both approaches. JCI is a causal
modeling framework rather than a specific algorithm, and it can be implemented
using any causal discovery algorithm that can take into account certain
background knowledge. JCI can deal with different types of interventions (e.g.,
perfect, imperfect, stochastic, etc.) in a unified fashion, and does not
require knowledge of intervention targets or types in case of interventional
data. We explain how several well-known causal discovery algorithms can be seen
as addressing special cases of the JCI framework, and we also propose novel
implementations that extend existing causal discovery methods for purely
observational data to the JCI setting. We evaluate different JCI
implementations on synthetic data and on flow cytometry protein expression data
and conclude that JCI implementations can considerably outperform
state-of-the-art causal discovery algorithms.Comment: Final version, as published by JML
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