Counterfactual Reasoning in Observational Studies

AAAI 2019 pp. 9886-9887

doi:10.1609/AAAI.V33I01.33019886 /aaai/2019/hassanpour2019aaai-counterfactual/

Abstract

To identify the appropriate action to take, an intelligent agent must infer the causal effects of every possible action choices. A prominent example is precision medicine that attempts to identify which medical procedure will benefit each individual patient the most. This requires answering counterfactual questions such as: ""Would this patient have lived longer, had she received an alternative treatment?"". In my PhD, I attempt to explore ways to address the challenges associated with causal effect estimation; with a focus on devising methods that enhance performance according to the individual-based measures (as opposed to population-based measures).

PDF AAAI Semantic Scholar

Cite

Text

Hassanpour. "Counterfactual Reasoning in Observational Studies." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019886

Markdown

[Hassanpour. "Counterfactual Reasoning in Observational Studies." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/hassanpour2019aaai-counterfactual/) doi:10.1609/AAAI.V33I01.33019886

BibTeX

@inproceedings{hassanpour2019aaai-counterfactual,
  title     = {{Counterfactual Reasoning in Observational Studies}},
  author    = {Hassanpour, Negar},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {9886-9887},
  doi       = {10.1609/AAAI.V33I01.33019886},
  url       = {https://mlanthology.org/aaai/2019/hassanpour2019aaai-counterfactual/}
}