Fair risk algorithms
RA Berk, AK Kuchibhotla… - Annual Review of …, 2023 - annualreviews.org
Machine learning algorithms are becoming ubiquitous in modern life. When used to help
inform human decision making, they have been criticized by some for insufficient accuracy …
inform human decision making, they have been criticized by some for insufficient accuracy …
Policy learning with asymmetric utilities
Data-driven decision making plays an important role even in high stakes settings like
medicine and public policy. Learning optimal policies from observed data requires a careful …
medicine and public policy. Learning optimal policies from observed data requires a careful …
Optimal and fair encouragement policy evaluation and learning
A Zhou - Advances in Neural Information Processing …, 2024 - proceedings.neurips.cc
In consequential domains, it is often impossible to compel individuals to take treatment, so
that optimal policy rules are merely suggestions in the presence of human non-adherence to …
that optimal policy rules are merely suggestions in the presence of human non-adherence to …
Policy learning with asymmetric counterfactual utilities
Data-driven decision making plays an important role even in high stakes settings like
medicine and public policy. Learning optimal policies from observed data requires a careful …
medicine and public policy. Learning optimal policies from observed data requires a careful …
Policy learning under biased sample selection
Practitioners often use data from a randomized controlled trial to learn a treatment
assignment policy that can be deployed on a target population. A recurring concern in doing …
assignment policy that can be deployed on a target population. A recurring concern in doing …
Experimental evaluation of algorithm-assisted human decision-making: Application to pretrial public safety assessment
Despite an increasing reliance on fully-automated algorithmic decision-making in our day-to-
day lives, humans still make consequential decisions. While the existing literature focuses …
day lives, humans still make consequential decisions. While the existing literature focuses …
Estimating and improving dynamic treatment regimes with a time-varying instrumental variable
Estimating dynamic treatment regimes (DTRs) from retrospective observational data is
challenging as some degree of unmeasured confounding is often expected. In this work, we …
challenging as some degree of unmeasured confounding is often expected. In this work, we …
Bayesian safe policy learning with chance constrained optimization: Application to military security assessment during the vietnam war
Algorithmic and data-driven decisions and recommendations are commonly used in high-
stakes decision-making settings such as criminal justice, medicine, and public policy. We …
stakes decision-making settings such as criminal justice, medicine, and public policy. We …
Distributionally robust causal inference with observational data
We consider the estimation of average treatment effects in observational studies and
propose a new framework of robust causal inference with unobserved confounders. Our …
propose a new framework of robust causal inference with unobserved confounders. Our …
Off-policy evaluation beyond overlap: partial identification through smoothness
Off-policy evaluation (OPE) is the problem of estimating the value of a target policy using
historical data collected under a different logging policy. OPE methods typically assume …
historical data collected under a different logging policy. OPE methods typically assume …