Causal Learning with Interactions


Toru Kitagawa, Mingli Chen

Date & Time

From: 11 December 2019
Until: 12 December 2019




The Institute for Fiscal Studies
7 Ridgmount Street,
Bookings are now closed. If you would like to attend this event please email to register.
Identification and estimation of causal effects are challenging in an environment where the agents interact through markets or social networks, since the standard framework of causal inference rules out the spillovers of the actions and outcomes among the subjects in the study. How to learn causal effects and design policies in the presence of spillovers are important topics of research with interdisciplinary interest.
This two-day workshop presents recent methodological advances and empirical applications on the topic in economics, epidemiology, and statistics. A special focus will be on the applications of tools in machine learning and computational statistics to causal inference with interacting agents. It aims to foster the exchange of ideas among different scientific communities including economics, epidemiology, machine learning, and statistics.

The speakers include
Vasco M. Carvalho (Cambridge)
Peng Ding (UC Berkeley)
Mirko Draca (Warwick)
Shin Kanaya (Aarhus)
Hyunseung Kang (Wisconsin)
Tetsuya Kaji (Chicago Booth)
Tyler H. McCormick (U Washington)
Kenichi Nagasawa (Warwick)

Elizabeth Ogburn (JHU)

Michele Pellizzari (Geneva)
Ashesh Rambachan (Harvard)
Fredrik Sävje (Yale)
Yuya Sasaki (Vanderbilt)
Rajen Shah (Cambridge)
Davide Viviano (UCSD)
This workshop is jointly funded by The Alan Turing Institute, CeMMAP, and ERC (grant no. 715940 – EPP)