Explore causal effect identification from multiple incomplete data sources in this 36-minute lecture by Dr Santtu Tikka from the University of Jyväskylä, Finland. Delve into the challenges of determining interventional probability distributions without parametric assumptions. Learn about a novel search algorithm utilizing do-calculus rules to address advanced data-generating mechanisms and various observational and experimental source distributions. Discover how this approach extends to causal inference under context-specific independence relations. Examine topics such as the data-fusion problem, identifiability issues in causal inference, missing data challenges, and context-specific independence. Gain insights into labeled directed acyclic graphs, CSI-separation, and the complexity of decision problems in causal effect identification. Explore practical examples, including case-control design and derivations, to solidify understanding of these complex concepts.
Causal Effect Identification from Multiple Incomplete Data Sources
Alan Turing Institute via YouTube
Overview
Syllabus
Intro
Starting point
The data-fusion problem
Identifiability problems in causal inference
The general identifiability problem
Motivation for a search-based approach
Search over the rules of do-calculus
Example on applying do-search
Missing data in causal inference
Example: case-control design.
Identifiability problems reassessed (with missing data)
Context-specific Independence
Alternative Representations for CSI
Labeled Directed Acyclic Graphs
Example on Context-specific DAGS
CSI-separation Example
Causal Effect Identification in LDAGS
Interventions in LDAGS
Complexity of the Decision Problem
Search over the rules of CSI-calculus
Search Example
Derivation of the Example
A Curious Example
Some Properties of the Search
Open Problems and Possible Future Work
References I
Taught by
Alan Turing Institute
