18999

Video URL

https://simons.berkeley.edu/talks/introduction-causal-discovery-methods-1

Unmeasured Confounding and More Recent Developments/Challenges in Causal Discovery

(2022). Unmeasured Confounding and More Recent Developments/Challenges in Causal Discovery. The Simons Institute for the Theory of Computing. https://simons.berkeley.edu/talks/introduction-causal-discovery-methods-1

Unmeasured Confounding and More Recent Developments/Challenges in Causal Discovery. The Simons Institute for the Theory of Computing, Jan. 19, 2022, https://simons.berkeley.edu/talks/introduction-causal-discovery-methods-1 

          @misc{ scivideos_18999,
            doi = {},
            url = {https://simons.berkeley.edu/talks/introduction-causal-discovery-methods-1},
            author = {},
            keywords = {},
            language = {en},
            title = {Unmeasured Confounding and More Recent Developments/Challenges in Causal Discovery},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {jan},
            note = {18999 see, \url{https://scivideos.org/Simons-Institute/18999}}
          }
Daniel Malinsky (Columbia University)
Talk number18999
Source RepositorySimons Institute
Subject

Abstract

This session will in part focus on the challenge of unmeasured confounding and some select approaches for meeting this challenge, e.g., learning mixed graphical models. We will also discuss more “modern” methods for causal discovery including ones that exploit semiparametric assumptions to perform model selection.