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About this paper
Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan
This research introduces an optimization framework for learning causal mechanisms within Structural Causal Models, allowing for the quantitative selection of mechanisms that minimize estimation variance for counterfactual queries. The approach, employing parameterized "gadgets," demonstrates improved efficiency over fixed mechanisms like Gumbel-max, particularly in reducing variance for counterfactual treatment effect estimates in MDPs.
Key insights
- Any causal mechanism in an SCM inherently defines an "implicit coupling" between interventional distributions by sharing exogenous noise.
- Fixed implicit couplings, including Gumbel-max, are not universally maximal and no single implicit coupling can be maximal for all distribution pairs in categorical spaces with three or more outcomes.
- By framing mechanism selection as an optimization problem, context-specific mechanisms can be learned to achieve superior statistical properties, such as variance reduction, compared to fixed axiomatic approaches.
Summary by alphaXiv.
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