Causally Aligned Active Learning by Prof Dino Sejdinovic

Abstract

We study how to estimate causal effects with fewer outcome measurements, which is essential when each measurement is costly. A central challenge is that the quantities we ultimately care about, such as potential outcomes and treatment effects, are not directly observable. As a result, standard active learning heuristics that target uncertainty in model parameters or factual predictions can be misaligned with the causal estimation objective. We present a Bayesian experimental design perspective on causally aligned data acquisition: at each step, we choose which data item to measure next by estimating its expected value for reducing posterior uncertainty about a target causal quantity. We first apply this principle to Conditional Average Treatment Effect (CATE) estimation, showing how acquisition rules can be built to focus directly on uncertainty in unobserved causal outcomes. We then broaden the scope beyond CATE to a general class of causal quantities, which can be expressed as integrals of regression functions, yielding a unified framework that supports acquisition strategies based on information gain. Together, these results clarify how aligning the acquisition objective with the desired causal estimand leads to more interpretable trade-offs, and how the best strategy depends on both the causal target and the structure of the data. Joint work with Erdun Gao and Jake Fawkes.

Speaker Profile

Dino Sejdinovic is a Professor of Statistical Machine Learning in the School of Mathematical Sciences, Adelaide University (since 2022), a Visiting Professor with the College of Computing and Data Science, Nanyang Technological University, Singapore (since 2025), and a Visiting Professor with the Research Center for Statistical Machine Learning, The Institute of Statistical Mathematics, Tokyo (since 2024). He was previously a Lecturer and an Associate Professor at the Department of Statistics, University of Oxford (2014-2022), a Fellow of Mansfield College, Oxford, and a Turing Faculty Fellow of the Alan Turing Institute. He held postdoctoral positions at the Gatsby Computational Neuroscience Unit, University College London (2011-2014) and at the Institute for Statistical Science, University of Bristol (2009-2011). He received a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research spans a wide variety of topics at the interface between machine learning and statistical methodology, including large-scale nonparametric and kernel methods, robust and trustworthy machine learning, causal inference, and uncertainty quantification.