83 citations · 100 across the 7 of their papers we have counts for
7 papers
An Introduction to Causal Reinforcement Learning
Elias Bareinboim, Junzhe Zhang, Sanghack Lee
Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i.e., w…
On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition
Inwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song +2
Conditional independence provides a way to understand causal relationships among the variables of interest. An underlying system may exhibit more fine-grained causal relationships…
Nested Counterfactual Identification from Arbitrary Surrogate Experiments
Juan D Correa, Sanghack Lee, Elias Bareinboim
The Ladder of Causation describes three qualitatively different types of activities an agent may be interested in engaging in, namely, seeing (observational), doing (interventional…
Towards Robust Relational Causal Discovery
Sanghack Lee, Vasant Honavar
We consider the problem of learning causal relationships from relational data. Existing approaches rely on queries to a relational conditional independence (RCI) oracle to establis…
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
Aria Khademi, Sanghack Lee, David Foley +1
As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become in…
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning
Sanghack Lee, Vasant Honavar
Maier et al. (2010) introduced the relational causal model (RCM) for representing and inferring causal relationships in relational data. A lifted representation, called abstract gr…
Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability
Sanghack Lee, Vasant Honavar
We introduce z-transportability, the problem of estimating the causal effect of a set of variables X on another set of variables Y in a target domain from experiments on any subset…