Tutorial on deep learning for causal inference

Speakers: Bernard Koch (SICSS-Los Angeles 19, 20, 21; Ph.D. student in Sociology at UCLA)

Description: This tutorial will teach participants how to build simple deep learning models for causal inference. Although this literature is still quite young, neural networks have the potential to extend causal inference to new settings where confounding is high-dimensional, non-linear, time-varying or even encoded in text, images, and networks. The tutorial will begin with 30 minutes of discussion on what deep learning is and how deep learning can be used for adjustment in observational causal inference, followed by 40 minutes of instruction on how to build your own custom deep learning models in Tensorflow 2. No prior knowledge of Tensorflow or neural networks is necessary, so this is also a great opportunity if you just want to get your feet wet with deep learning for the first time.

Tutorial: https://colab.research.google.com/dri...