B0739
Title: Confidence sets for causal orderings
Authors: Mladen Kolar - University of Chicago (United States)
Mathias Drton - Technical University of Munich (Germany)
Y Samuel Wang - Cornell University (United States) [presenting]
Abstract: Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal model or a single equivalence class of models, less attention has been given to quantifying uncertainty in causal discovery in terms of confidence statements. The primary challenge in causal discovery is determining a causal ordering among the variables. The research offers a framework for constructing confidence sets of causal orderings that the data do not rule out. The methodology applies to structural equation models and is based on a residual bootstrap procedure to test the goodness-of-fit of causal orderings. The asymptotic validity of the confidence set constructed using this goodness-of-fit test is demonstrated and explains how the confidence set may be used to form sub/supersets of ancestral relationships as well as confidence intervals for causal effects that incorporate model uncertainty.
