no code implementations • ICML 2020 • Sanghack Lee, Elias Bareinboim
Finally, building on these graphical properties, we develop an algorithm that returns a formula for a causal effect in terms of the available distributions.
1 code implementation • 12 May 2024 • Inwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song, Byoung-Tak Zhang, Sanghack Lee
Conditional independence provides a way to understand causal relationships among the variables of interest.
no code implementations • 19 Nov 2023 • Chanhui Lee, Juhyeon Kim, Yongjun Jeong, Juhyun Lyu, Junghee Kim, Sangmin Lee, Sangjun Han, Hyeokjun Choe, Soyeon Park, Woohyung Lim, Sungbin Lim, Sanghack Lee
Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning.
no code implementations • NeurIPS 2021 • Sanghack Lee, Elias Bareinboim
Causal effect identification is concerned with determining whether a causal effect is computable from a combination of qualitative assumptions about the underlying system (e. g., a causal graph) and distributions collected from this system.
no code implementations • NeurIPS 2021 • Juan D Correa, Sanghack Lee, Elias Bareinboim
In this paper, we study the identification of nested counterfactuals from an arbitrary combination of observations and experiments.
no code implementations • NeurIPS 2020 • Sanghack Lee, Elias Bareinboim
Intelligent agents are continuously faced with the challenge of optimizing a policy based on what they can observe (see) and which actions they can take (do) in the environment where they are deployed.
1 code implementation • 5 Dec 2019 • Sanghack Lee, Vasant Honavar
In practice, queries to a RCI oracle have to be replaced by reliable tests for RCI against available data.
no code implementations • 27 Mar 2019 • Aria Khademi, Sanghack Lee, David Foley, Vasant Honavar
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 instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc.
1 code implementation • NeurIPS 2018 • Sanghack Lee, Elias Bareinboim
We study the problem of identifying the best action in a sequential decision-making setting when the reward distributions of the arms exhibit a non-trivial dependence structure, which is governed by the underlying causal model of the domain where the agent is deployed.
no code implementations • 10 Aug 2015 • Sanghack Lee, Vasant Honavar
The correctness of the algorithm proposed by Maier et al. (2013a) for learning RCM from data relies on the soundness and completeness of AGG for relational d-separation to reduce the learning of an RCM to learning of an AGG.
no code implementations • NeurIPS 2013 • Elias Bareinboim, Sanghack Lee, Vasant Honavar, Judea Pearl
This paper considers the problem of transferring experimental findings learned from multiple heterogeneous domains to a target environment, in which only limited experiments can be performed.
no code implementations • 26 Sep 2013 • Sanghack Lee, Vasant Honavar
We provide a correct and complete algorithm that determines whether a causal effect is z-transportable; and if it is, produces a transport formula, that is, a recipe for estimating the causal effect of X on Y in the target domain using information elicited from the results of experimental manipulations of Z in the source domain and observational data from the target domain.