# P2C: Path to Counterfactuals

## TL;DR

P2C introduces a causal, sequential planning framework for generating realistic, actionable counterfactual explanations in high-stakes decision-making, addressing limitations of existing methods by modeling causal dependencies and feasible intervention sequences.

## Contribution

The paper presents P2C, a novel, model-agnostic approach that produces causally consistent, sequential plans for counterfactual explanations, incorporating causal dependencies and realistic intervention sequences.

## Key findings

- P2C outperforms standard planners in generating legal, feasible plans.
- P2C provides more realistic cost estimates by focusing on user-initiated changes.
- The framework effectively models causal relationships and sequential interventions.

## Abstract

Machine-learning models are increasingly driving decisions in high-stakes settings, such as finance, law, and hiring, thus, highlighting the need for transparency. However, the key challenge is to balance transparency -- clarifying `why' a decision was made -- with recourse: providing actionable steps on `how' to achieve a favourable outcome from an unfavourable outcome. Counterfactual explanations reveal `why' an undesired outcome occurred and `how' to reverse it through targeted feature changes (interventions). Current counterfactual approaches have limitations: 1) they often ignore causal dependencies between features, and 2) they typically assume all interventions can happen simultaneously, an unrealistic assumption in practical scenarios where actions are typically taken in a sequence. As a result, these counterfactuals are often not achievable in the real world. We present P2C (Path-to-Counterfactuals), a model-agnostic framework that produces a plan (ordered sequence of actions) converting an unfavourable outcome to a causally consistent favourable outcome. P2C addresses both limitations by 1) Explicitly modelling causal relationships between features and 2) Ensuring that each intermediate state in the plan is feasible and causally valid. P2C uses the goal-directed Answer Set Programming system s(CASP) to generate the plan accounting for feature changes that happen automatically due to causal dependencies. Furthermore, P2C refines cost (effort) computation by only counting changes actively made by the user, resulting in realistic cost estimates. Finally, P2C highlights how its causal planner outperforms standard planners, which lack causal knowledge and thus can generate illegal actions.