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Raghav Singal

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3 papers

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JMLR Journal 2024 Journal Article

Axiomatic effect propagation in structural causal models

Raghav Singal
George Michailidis

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). [abs] [ pdf ][ bib ] &copy JMLR 2024. ( edit, beta )

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ICML Conference 2023 Conference Paper

Counterfactual Analysis in Dynamic Latent State Models

Martin B. Haugh
Raghav Singal

We provide an optimization-based framework to perform counterfactual analysis in a dynamic model with hidden states. Our framework is grounded in the “abduction, action, and prediction” approach to answer counterfactual queries and handles two key challenges where (1) the states are hidden and (2) the model is dynamic. Recognizing the lack of knowledge on the underlying causal mechanism and the possibility of infinitely many such mechanisms, we optimize over this space and compute upper and lower bounds on the counterfactual quantity of interest. Our work brings together ideas from causality, state-space models, simulation, and optimization, and we apply it on a breast cancer case study. To the best of our knowledge, we are the first to compute lower and upper bounds on a counterfactual query in a dynamic latent-state model.

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ICML Conference 2021 Conference Paper

Flow-based Attribution in Graphical Models: A Recursive Shapley Approach

Raghav Singal
George Michailidis
Hoiyi Ng

We study the attribution problem in a graphical model, wherein the objective is to quantify how the effect of changes at the source nodes propagates through the graph. We develop a model-agnostic flow-based attribution method, called recursive Shapley value (RSV). RSV generalizes a number of existing node-based methods and uniquely satisfies a set of flow-based axioms. In addition to admitting a natural characterization for linear models and facilitating mediation analysis for non-linear models, RSV satisfies a mix of desirable properties discussed in the recent literature, including implementation invariance, sensitivity, monotonicity, and affine scale invariance.

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