"AN OVERVIEW OF CAUSAL INFERENCE USING KERNEL EMBEDDINGS"
Nonparametric approach makes causal inference possible in complex real-world scenarios
And Kernel embeddings enable causal inference without restrictive parametric assumptions.
https://arxiv.org/abs/2410.22754
🎯 Original Problem:
Causal inference in complex real-world datasets faces challenges with traditional parametric methods that rely on restrictive assumptions. We need better ways to estimate causal effects from observational data.
-----
🔧 The paper explores kernel embeddings as a nonparametric framework for causal inference. By mapping probability measures into reproducing kernel Hilbert spaces (RKHS), it enables:
→ Flexible representation of complex variable relationships without parametric assumptions
→ Seamless transformation from observational to interventional distributions
→ Unified framework for estimating Average Treatment Effect (ATE) and Distributional Treatment Effect (DTE)
→ Integration of multiple datasets through causal data fusion
-----
💡 Key Insights:
→ Kernel embeddings can handle both discrete and continuous variables
→ The framework works across multiple causal inference settings: backdoor adjustment, frontdoor adjustment, instrumental variables
→ Combines benefits of both potential outcomes and graphical causal inference approaches
→ Can propagate uncertainty through Bayesian kernel embeddings
