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Found 8 papers, 1 papers with code
Does AI help humans make better decisions? A methodological framework for experimental evaluation
We consider a single-blinded experimental design, in which the provision of AI-generated recommendations is randomized across cases with a human making final decisions.
Optimizing Language Models for Human Preferences is a Causal Inference Problem
In this paper, we present an initial exploration of language model optimization for human preferences from direct outcome datasets, where each sample consists of a text and an associated numerical outcome measuring the reader's response.
Temporal Aggregation for the Synthetic Control Method
The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit with panel data.
Using Multiple Outcomes to Improve the Synthetic Control Method
When there are multiple outcome series of interest, Synthetic Control analyses typically proceed by estimating separate weights for each outcome.
Text-Transport: Toward Learning Causal Effects of Natural Language
To address this issue, we leverage the notion of distribution shift to describe an estimator that transports causal effects between domains, bypassing the need for strong assumptions in the target domain.
Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War
First, before implementing a new algorithm, it is essential to characterize and control the risk of yielding worse outcomes than the existing algorithm.
Policy Learning with Asymmetric Counterfactual Utilities
We consider optimal policy learning with asymmetric counterfactual utility functions of this form that consider the joint set of potential outcomes.
Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment
We extend this approach to common and important settings where humans make decisions with the aid of algorithmic recommendations.
