Learning Actionable Counterfactual Explanations in Large State Spaces

Keziah Naggita, Matthew Walter, Avrim Blum

Abstract:

Recourse generators provide actionable insights, often through feature-based counterfactual explanations (CFEs), to help negatively classified individuals understand how to adjust their input features to achieve a positive classification. These feature-based CFEs, which we refer to as \emph{low-level} CFEs, are overly specific (e.g., coding experience: \(4 \to 5+\) years) and often recommended in a feature space that doesn't straightforwardly align with real-world actions. To bridge this gap, we introduce three novel recourse types grounded in real-world actions: high-level continuous (\emph{hl-continuous}), high-level discrete (\emph{hl-discrete}), and high-level ID (\emph{hl-id}) CFEs.

We formulate single-agent CFE generation methods for hl-discrete and hl-continuous CFEs. For the hl-discrete CFE, we cast the task as a weighted set cover problem that selects the least cost set of hl-discrete actions that satisfy the eligibility of features, and model the hl-continuous CFE as a solution to an integer linear program that identifies the least cost set of hl-continuous actions capable of favorably altering the prediction of a linear classifier. Since these methods require costly optimization per agent, we propose data-driven CFE generation approaches that, given instances of agents and their optimal CFEs, learn a CFE generator that quickly provides optimal CFEs for new agents. This approach, also viewed as one of learning an optimal policy in a family of large but deterministic MDPs, considers several problem formulations, including formulations in which the actions and their effects are unknown, and therefore addresses informational and computational challenges.

We conduct extensive empirical evaluations using publicly available healthcare datasets (BRFSS, Foods, and NHANES) and fully-synthetic data. For negatively classified agents identified by linear and threshold-based binary classifiers, we compare the proposed forms of recourse to low-level CFEs, which suggest how the agent can transition from state \(\mathbf{x}\) to a new state \(\mathbf{x}'\) where the model prediction is desirable. We also extensively evaluate the effectiveness of our neural network-based, data-driven CFE generation approaches. Empirical results show that the proposed data-driven CFE generators are accurate and resource-efficient, and the proposed forms of recourse offer various advantages over the low-level CFEs.

Submission Length: Regular submission (no more than 12 pages of main content)

Code: https://github.com/ripl/LAR-LSS

Assigned Action Editor: ~Taylor_W._Killian1

Submission Number: 4593