Research map / Probabilistic and scientific modeling / Causal discovery
Improvement-Focused Causal Recourse (ICR)
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| target improvement in the underlying outcome rather than acceptance by a particular predictor | ICR vs CE and CR · 3var-noncausal | improvement | Helped |
| target improvement in the underlying outcome rather than acceptance by a particular predictor | ICR vs CE and CR · 5var-skill | improvement | Helped |
| target improvement in the underlying outcome rather than acceptance by a particular predictor | ICR vs CE and CR · 7var-covid | improvement | Helped |
| use acceptance-focused causal recourse | CR · 3var-causal | improvement: close to 50% | Helped |
| use improvement-focused actions and post-recourse prediction | ICR · evaluated datasets | acceptance | Helped |
| use improvement-focused recourse instead of acceptance-focused recourse | ICR vs CE and CR · evaluated datasets, refits on the same data | acceptance after model refits | Helped |
| recommend actions that improve the underlying target rather than actions that only change model predictions | ICR vs CR · 5var-skill | recourse cost | Hurt |
Closest papers by meaning
- Algorithmic Recourse for Long-Term Improvement (ICML 2025 · similarity 0.88)
- Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (NeurIPS 2020 · similarity 0.88)
- Performative Validity of Recourse Explanations (NeurIPS 2025 · similarity 0.86)
- SafeAR: Safe Algorithmic Recourse by Risk-Aware Policies (AAAI 2024 · similarity 0.84)
- Incentivizing Desirable Effort Profiles in Strategic Classification: The Role of Causality and Uncertainty (NeurIPS 2025 · similarity 0.82)
- Causality can systematically address the monsters under the bench(marks) (NeurIPS 2026 · similarity 0.82)
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