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Axiomatic effect propagation in structural causal models

Raghav Singal, George Michailidis; 25(52):1−71, 2024.


We study effect propagation in a causal directed acyclic graph (DAG), with the goal of providing a flow-based decomposition of the effect (i.e., change in the outcome variable) as a result of changes in the source variables. We first compare various ideas on causality to quantify effect propagation, such as direct and indirect effects, path-specific effects, and degree of responsibility. We discuss the shortcomings of such approaches and propose a flow-based methodology, which we call recursive Shapley value (RSV). By considering a broader set of counterfactuals than existing methods, RSV obeys a unique adherence to four desirable flow-based axioms. Further, we provide a general path-based characterization of RSV for an arbitrary non-parametric structural equations model (SEM) defined on the underlying DAG. Interestingly, for the special class of linear SEMs, RSV exhibits a simple and tractable characterization (and hence, computation), which recovers the classical method of path coefficients and is equivalent to path-specific effects. For non-parametric SEMs, we use our general characterization to develop an unbiased Monte-Carlo estimation procedure with an exponentially decaying sample complexity. We showcase the application of RSV on two challenging problems on causality (causal overdetermination and causal unfairness).

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