#NeurIPS2020 invited talks round-up: part three – causal learning and the genomic bottleneck

In this post we conclude our summaries of the NeurIPS invited talks from the 2020 meeting. In this final instalment, we cover the talks by Marloes Maathuis (ETH Zurich) and Anthony M Zador (Cold Spring Harbor Laboratory). Marloes began her talk on causal learning with a simple example of the phenomenon known as Simpson's paradox, in which a trend appears in several different groups of data but disappears or reverses when these groups are combined. She also talked about the importance of considering causality when making decisions based on such data. Marloes went on to explain the difference between causal and non-causal questions. Non-causal questions are about predictions in the same system, for example, predicting the cancer rate among smokers.