43 citations · 101 across the 10 of their papers we have counts for
10 papers
Bridging Predictions and Interventions: An Integrated Framework for Automated Decision-Systems
Inioluwa Deborah Raji, Lydia T. Liu, Angela Zhou +27
Automated decision systems (ADS) leverage predictions about individual future outcomes to inform consequential decision-making in organizational settings. Across various settings -…
Exploiting independence constraints for efficient estimation of bounds on causal effects in the presence of unmeasured confounding
Ting-Hsuan Chang, Caleb H. Miles, Ilya Shpitser +2
Causal graphs may inform covariate adjustment for estimating causal effects and improve estimation efficiency by exploiting the graphical structure. In many applications, however,…
Bridging Prediction and Intervention Problems in Social Systems
Lydia T. Liu, Inioluwa Deborah Raji, Angela Zhou +32
Many automated decision systems (ADS) are designed to solve prediction problems -- where the goal is to learn patterns from a sample of the population and apply them to individuals…
Causal Inference With Outcome-Dependent Missingness And Self-Censoring
Jacob M Chen, Daniel Malinsky, Rohit Bhattacharya
We consider missingness in the context of causal inference when the outcome of interest may be missing. If the outcome directly affects its own missingness status, i.e., it is "sel…
Differentiable Causal Discovery Under Unmeasured Confounding
Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky +1
The data drawn from biological, economic, and social systems are often confounded due to the presence of unmeasured variables. Prior work in causal discovery has focused on discret…
Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning
Numair Sani, Daniel Malinsky, Ilya Shpitser
Causal approaches to post-hoc explainability for black-box prediction models (e.g., deep neural networks trained on image pixel data) have become increasingly popular. However, exi…