Causal Discovery Grounding and the Naturalness Assumption with Ricardo Silva

Abstract:

Causal discovery is the process of learning structural information that allows for the inference of causal effects using observational data. Standard methods postulate that causal structure can be described by a directed acyclic graph (DAG), which can be partially reconstructed by using the faithfulness principle. According to this principle, conditional independencies in the observational distribution correspond exactly to those encoded in the unknown DAG.

However, causal discovery algorithms are challenged by the fact that, without even stronger assumptions, approximate failures of faithfulness are empirically undistinguishable from exact ones. This leaves an inferential gap, as exact failures of faithfulness can lead to major implications to causal effect identification. Moreover, in many applications, it is not realistic to expect that exact conditional independencies hold among most recorded variables: they are often just proxies to unobservable manipulable factors, so that there is no structural justification to expect such independencies to emerge.

The above suggests a different perspective, reliant neither on exact independence nor on the distinction between approximate and exact faithfulness. By considering the specialized problem of discovering whether unmeasured confounding can be ruled out for a particular cause-effect pair, we introduce a principle we call naturalness, in which the strength of observational associations bounds the strength of causal dependencies. The bounding function is unknown, with degrees of freedom not present in faithfulness. We model uncertainty about this function using a Bayesian framing.

To go beyond uncertainty which is purely theory-driven, we present an algorithm that grounds it using experimental data from past studies, resulting on adaptive causal discovery principle. We demonstrate its usefulness in providing partial identification of causal effects compared to other methods that relax the assumption of no unmeasured confounding, and discuss how the promising results can motivate new principles for general causal discovery.

Bio:

Ricardo is a Professor of Statistical Machine Learning and Data Science at University College London, department of Statistical Science, and a member of the UCL AI Centre. He holds an Open Fellowship from the Engineering and Physical Sciences Research Council (EPSRC, UK), as well as being a co-lead of the multi-institutional Causality in Healthcare AI Hub (CHAI) funded by EPSRC, and a co-investigator in the UCL Science of Fundamental AI Research (SOFAIR) Lab. Ricardo’s research has touched many aspects of uncertainty in causal modelling, including issues of measurement error, uncertainty in identifiability, and Bayesian inference for integrating multiple sources of evidence. He was one of the Programme co-Chairs of the Uncertainty in Artificial Intelligence Conference (UAI) in 2018, and Conference co-Chair of UAI 2019, besides being a co-organizer of multiple workshops along the years in conferences such as NeurIPS, UAI and ICML.