6 citations · 8 across the 5 of their papers we have counts for
12 papers
HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach
Sainyam Galhotra, Amir Gilad, Sudeepa Roy +1
What-if (provisioning for an update to a database) and how-to (how to modify the database to achieve a goal) analyses provide insights to users who wish to examine hypothetical sce…
Heterogeneous Treatment Effects in Social Networks
Amir Gilad, Harsh Parikh, Sudeepa Roy +1
We study treatment effect modifiers for causal analysis in a social network, where neighbors' characteristics or network structure may affect the outcome of a unit, and the goal is…
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals
Sainyam Galhotra, Romila Pradhan, Babak Salimi
There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that aims to reduce the opaqueness of AI-based decision-making systems, allowing humans…
Causal Relational Learning
Babak Salimi, Harsh Parikh, Moe Kayali +3
Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard in ca…
Mining Approximate Acyclic Schemes from Relations
Batya Kenig, Pranay Mundra, Guna Prasad +2
Acyclic schemes have numerous applications in databases and in machine learning, such as improved design, more efficient storage, and increased performance for queries and machine…
Data Management for Causal Algorithmic Fairness
Babak Salimi, Bill Howe, Dan Suciu
Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflects d…