Causal inference yesterday, today and tomorrow (PI-IVADO-IC Special Webinar)
APA
Shpitser, I. (2024). Causal inference yesterday, today and tomorrow (PI-IVADO-IC Special Webinar). Perimeter Institute. https://pirsa.org/24090157
MLA
Shpitser, Ilya. Causal inference yesterday, today and tomorrow (PI-IVADO-IC Special Webinar). Perimeter Institute, Sep. 13, 2024, https://pirsa.org/24090157
BibTex
@misc{ pirsa_PIRSA:24090157, doi = {}, url = {https://pirsa.org/24090157}, author = {Shpitser, Ilya}, keywords = {Other}, language = {en}, title = {Causal inference yesterday, today and tomorrow (PI-IVADO-IC Special Webinar)}, publisher = {Perimeter Institute}, year = {2024}, month = {sep}, note = {PIRSA:24090157 see, \url{https://pirsa.org}} }
Johns Hopkins University
Talk number
PIRSA:24090157
Abstract
As part of a monthly webinar series jointly hosted by Perimeter, IVADO, and Institut Courtois, Ilya Shpitser will present an introduction to causal inference and its applications to problems in physics and computer science. This seminar will be fully on zoom and members of all three institutes are welcome.
Abstract: In this talk I will give some history of ideas of causal inference, describe the causal inference workflow, including formalizing the cause-effect question in terms of a parameter, defining (or learning) the causal model, checking if the data has information about the desired parameter via identification theory, and efficiently estimating the parameter if it is identified. I will briefly touch on connections of causal inference to other areas, discuss what machine learning and causal inference can teach each other, and describe some open problems. Zoom TBC
Abstract: In this talk I will give some history of ideas of causal inference, describe the causal inference workflow, including formalizing the cause-effect question in terms of a parameter, defining (or learning) the causal model, checking if the data has information about the desired parameter via identification theory, and efficiently estimating the parameter if it is identified. I will briefly touch on connections of causal inference to other areas, discuss what machine learning and causal inference can teach each other, and describe some open problems. Zoom TBC