Causal Inference - Machine learning as a tool for evidence-based policy
Machine learning (ML) can be a useful tool for observational causal inference studies, one of the cornerstones of evidence-based policy. ML can help us capture complex relationships in the data, thereby helping mitigate bias from model mis-specification. Also, use of regularisation in machine learning can lead to causal estimates with less error compared to unbiased methods when we have many related confounding factors in our data. I helped to write a blog post on this subject, and at Gradient Institute we have used machine learning for observational studies such as linking youth well-being to academic success. Reporting non-linear causal effects requires a new methodology, software for which we developed and can be found here.
