TY - JOUR AU - Pensar, Johan AU - Talvitie, Topi AU - Hyttinen, Antti AU - Koivisto, Mikko PY - 2020/04/03 Y2 - 2024/03/28 TI - A Bayesian Approach for Estimating Causal Effects from Observational Data JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 04 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v34i04.5988 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5988 SP - 5395-5402 AB - <p>We present a novel Bayesian method for the challenging task of estimating causal effects from passively observed data when the underlying causal DAG structure is unknown. To rigorously capture the inherent uncertainty associated with the estimate, our method builds a Bayesian posterior distribution of the linear causal effect, by integrating Bayesian linear regression and averaging over DAGs. For computing the exact posterior for all cause-effect variable pairs, we give an algorithm that runs in time <em>O</em>(3<sup><em>d</em></sup> <em>d</em>) for <em>d</em> variables, being feasible up to 20 variables. We also give a variant that computes the posterior probabilities of all pairwise ancestor relations within the same time complexity, significantly improving the fastest previous algorithm. In simulations, our Bayesian method outperforms previous methods in estimation accuracy, especially for small sample sizes. We further show that our method for effect estimation is well-adapted for detecting strong causal effects markedly deviating from zero, while our variant for computing posteriors of ancestor relations is the method of choice for detecting the mere existence of a causal relation. Finally, we apply our method on observational flow cytometry data, detecting several causal relations that concur with previous findings from experimental data.</p> ER -