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Creators/Authors contains: "A. Li and J. Pearl"

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  1. (, Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22))
    Vasant Honavar and Matthijs Spaan (Ed.)
    This paper addresses the problem of estimating causal effects when adjustment variables in the back-door or front-door criterion are partially observed. For such scenarios, we derive bounds on the causal effects by solving two non-linear optimization problems, and demonstrate that the bounds are sufficient. Using this optimization method, we propose a framework for dimensionality reduction that allows one to trade bias for estimation power, and demonstrate its performance using simulation studies. 
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    Accepted Manuscript Full Text Available
  2. (, Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22))
    Vasant Honavar and Matthijs Spaan (Ed.)
    The unit selection problem aims to identify a set of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if encouraged and a different way if not encouraged. Using a combination of experimental and observational data, Li and Pearl derived tight bounds on the “benefit function” - the payoff/cost associated with selecting an individual with given characteristics. This paper shows that these bounds can be narrowed significantly (enough to change decisions) when structural information is available in the form of a causal model. We address the problem of estimating the benefit function using observational and experimental data when specific graphical criteria are assumed to hold. 
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    Accepted Manuscript Full Text Available