Causal Inference from Observational Data in Partially Understood Domains

Date

Aug 30, 2022

Time

1:00 PM - 2:00 PM

Speaker

Adèle Ribeiro

Affiliation

Causal AI Lab, Columbia University

Language

en

Main Topic

Biologie

Host

Ivo Sbalzarini

Description

One pervasive task found throughout the empirical sciences is to determine the effect of interventions from observational (non-experimental) data. It is well-understood that assumptions are necessary to perform causal inferences, which are commonly articulated through causal diagrams (Pearl, 2000). Despite the power of this approach, there are settings where the knowledge necessary to fully specify a causal diagram may not be available, particularly in complex, high-dimensional domains. In this talk, I will present two novel causal effect identification approaches that relax the stringent requirement of fully specifying a causal diagram. The first is a new graphical modeling tool called cluster DAGs (for short, C-DAGs) that allows for the specification of relationships among clusters of variables, while the relationships between the variables within a cluster are left unspecified. The second includes a complete calculus and algorithm for effect identification from a Partial Ancestral Graph (PAG), which represents a Markov equivalence class of causal diagrams, learnable from observational data. These approaches are expected to help researchers and data scientists to identify novel effects in real-world domains, where knowledge is largely unavailable and coarse.

Last modified: Aug 31, 2022, 12:05:29 AM