8 citations · 19 across the 11 of their papers we have counts for
12 papers
Learning What to Forget: Distributional Unlearning for LLM Representation Spaces
Pinaki Mohanty, Haoran Tang, Maggie Makar +1
Machine learning systems increasingly face the need to remove the influence of entire data domains, such as toxic language, harmful behavior, or topical content, rather than isolat…
Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
Donna Tjandra, Trenton Chang, Sonali Parbhoo +8
Objective: The growing availability of large-scale observational clinical datasets and challenges in conducting randomized controlled trials have spurred enthusiasm in using causal…
Estimating Misreporting in the Presence of Genuine Modification: A Causal Perspective
Dylan Zapzalka, Trenton Chang, Lindsay Warrenburg +5
In settings where ML models are used to inform the allocation of resources, agents affected by the allocation decisions might have an incentive to strategically change their featur…
Who's Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation
Trenton Chang, Lindsay Warrenburg, Sae-Hwan Park +3
In many settings, machine learning models may be used to inform decisions that impact individuals or entities who interact with the model. Such entities, or agents, may game model…
Partial identification of kernel based two sample tests with mismeasured data
Ron Nafshi, Maggie Makar
Nonparametric two-sample tests such as the Maximum Mean Discrepancy (MMD) are often used to detect differences between two distributions in machine learning applications. However,…
Multi-Similarity Contrastive Learning
Emily Mu, John Guttag, Maggie Makar
Given a similarity metric, contrastive methods learn a representation in which examples that are similar are pushed together and examples that are dissimilar are pulled apart. Cont…
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
Shengpu Tang, Maggie Makar, Michael W. Sjoding +2
Many reinforcement learning (RL) applications have combinatorial action spaces, where each action is a composition of sub-actions. A standard RL approach ignores this inherent fact…
Fairness and robustness in anti-causal prediction
Maggie Makar, Alexander D'Amour
Robustness to distribution shift and fairness have independently emerged as two important desiderata required of modern machine learning models. While these two desiderata seem rel…
Causally motivated Shortcut Removal Using Auxiliary Labels
Maggie Makar, Ben Packer, Dan Moldovan +3
Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning. We study a flexible, causally-motiv…
Estimation of Bounds on Potential Outcomes For Decision Making
Maggie Makar, Fredrik D. Johansson, John Guttag +1
Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often…
Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data
Maggie Makar, Marzyeh Ghassemi, David Cutler +1
Risk prediction is central to both clinical medicine and public health. While many machine learning models have been developed to predict mortality, they are rarely applied in the…
Learning the Probability of Activation in the Presence of Latent Spreaders
Maggie Makar, John Guttag, Jenna Wiens
When an infection spreads in a community, an individual's probability of becoming infected depends on both her susceptibility and exposure to the contagion through contact with oth…