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Found 8 papers, 7 papers with code
Finding Counterfactually Optimal Action Sequences in Continuous State Spaces
Whenever a clinician reflects on the efficacy of a sequence of treatment decisions for a patient, they may try to identify critical time steps where, had they made different decisions, the patient's health would have improved.
On the Within-Group Fairness of Screening Classifiers
Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes.
Counterfactual Explanations in Sequential Decision Making Under Uncertainty
In this work, we initiate the development of methods to find counterfactual explanations for decision making processes in which multiple, dependent actions are taken sequentially over time.
Group Testing under Superspreading Dynamics
Testing is recommended for all close contacts of confirmed COVID-19 patients.
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Decision-making systems increasingly orchestrate our world: how to intervene on the algorithmic components to build fair and equitable systems is therefore a question of utmost importance; one that is substantially complicated by the context-dependent nature of fairness and discrimination.
Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots
Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19.
Decisions, Counterfactual Explanations and Strategic Behavior
In this paper, our goal is to find policies and counterfactual explanations that are optimal in terms of utility in such a strategic setting.
Optimal Decision Making Under Strategic Behavior
Using this characterization, we first show that, in general, we cannot expect to find optimal decision policies in polynomial time and there are cases in which deterministic policies are suboptimal.
