Linear-Time Algorithms for Front-Door Adjustment in Causal Graphs

Abstract: Causal effect estimation from observational data is a fundamental task in empirical sciences. It becomes particularly challenging when unobserved confounders are involved in a system. Front-door adjustment constitutes a classic method that allows identifying the causal effect even in the presence of latent confounding by using observed mediators. This talk presents a recent algorithmic result in this area, namely a linear-time algorithm for finding a front-door adjustment set in a given causal graph. Its run-time is asymptotically optimal and improves on the previous state-of-the-art for this task by a factor that grows cubically in the number of variables. Beyond this result, the presentation explores fundamental algorithmic tools and techniques useful for broader applications in causal inference.

machine learningprobabilitystatistics theory

Audience: researchers in the discipline

( paper )

Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk). Speakers are asked to prepare material for 35 minutes excluding questions from the audience.