#ICML2022 invited talk round-up 2: estimating causal effects and drug discovery and development

In this post, we summarise the final two invited talks from the International Conference on Machine Learning (ICML 2022). These presentations covered estimation and inference for causal effects, and machine learning for drug discovery and development. Guido's talk covered the topic of estimation and inference for causal effects in panel data settings, in particular focussing on synthetic control methods and difference-in-difference methods. These methods are very popular in the empirical literature in economics, but many questions remain concerning causal effects in these settings. There has been a lot of recent theoretical work trying to improve practices in this field.