# Synthesizing Action Sequences for Modifying Model Decisions

arXiv: https://arxiv.org/abs/1910.00057 · HTML: https://mlsanity.com/paper/1910.00057/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 22 of 22)

- [If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques](https://mlsanity.com/paper/2103.01035/) (2021)
- [Amortized Generation of Sequential Algorithmic Recourses for Black-box Models](https://mlsanity.com/paper/2106.03962/) (2021)
- [A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data](https://mlsanity.com/paper/2107.04680/) (2021)
- [Consequence-aware Sequential Counterfactual Generation](https://mlsanity.com/paper/2104.05592/) (2021)
- [Achieving Diversity in Counterfactual Explanations: a Review and Discussion](https://mlsanity.com/paper/2305.05840/) (2023)
- [Personalized Algorithmic Recourse with Preference Elicitation](https://mlsanity.com/paper/2205.13743/) (2022)
- [Generating collective counterfactual explanations in score-based classification via mathematical optimization](https://mlsanity.com/paper/2310.12822/) (2023)
- [Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis](https://mlsanity.com/paper/2201.07135/) (2022)
- [Exploiting Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Exploration](https://mlsanity.com/paper/2404.05270/) (2024)
- [Feasible Recourse Plan via Diverse Interpolation](https://mlsanity.com/paper/2302.11213/) (2023)
- [Navigating Explanatory Multiverse Through Counterfactual Path Geometry](https://mlsanity.com/paper/2306.02786/) (2023)
- [Prediction without Preclusion: Recourse Verification with Reachable Sets](https://mlsanity.com/paper/2308.12820/) (2023)
- [Trustworthy Actionable Perturbations](https://mlsanity.com/paper/2405.11195/) (2024)
- [Unveiling the Potential of Counterfactuals Explanations in Employability](https://mlsanity.com/paper/2305.10069/) (2023)
- [Perfect Counterfactuals in Imperfect Worlds: Modelling Noisy Implementation of Actions in Sequential Algorithmic Recourse](https://mlsanity.com/paper/2410.02273/) (2024)
- [Iterative Partial Fulfillment of Counterfactual Explanations: Benefits and Risks](https://mlsanity.com/paper/2303.11111/) (2023)
- [Learning Actionable Counterfactual Explanations in Large State Spaces](https://mlsanity.com/paper/2404.17034/) (2024)
- [Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning](https://mlsanity.com/paper/2311.00523/) (2023)
- [Explainable History Distillation by Marked Temporal Point Process](https://mlsanity.com/paper/2311.07797/) (2023)
- [Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation](https://mlsanity.com/paper/2402.15073/) (2024)
- [Counterfactual optimization for fault prevention in complex wind energy systems](https://mlsanity.com/paper/2507.08849/) (2025)
- [Learning Marked Temporal Point Process Explanations based on Counterfactual and Factual Reasoning](https://mlsanity.com/paper/2508.11943/) (2025)

## Abstract (arXiv)

When a model makes a consequential decision, e.g., denying someone a loan, it needs to additionally generate actionable, realistic feedback on what the person can do to favorably change the decision. We cast this problem through the lens of program synthesis, in which our goal is to synthesize an optimal (realistically cheapest or simplest) sequence of actions that if a person executes successfully can change their classification. We present a novel and general approach that combines search-based program synthesis and test-time adversarial attacks to construct action sequences over a domain-specific set of actions. We demonstrate the effectiveness of our approach on a number of deep neural networks.