Adversarial machine learning and instrumental variables for flexible causal modeling
We are going through a new shift in machine learning (ML), where ML models are increasingly being used to automate decision-making in a multitude of domains: what personalized treatment should be administered to a patient, what discount should be offered to an online customer, and other important decisions that can greatly impact people’s lives.
The machine learning revolution was primarily driven by problems that are distant from such decision-making scenarios. The first scenarios include predicting what an image depicts, predicting the meaning of an English text, or predicting the next frame in a video sequence. This begs the question: is the same hammer used to enable these high-accuracy predictive models equally good enough to drive the nail in automated decision-making? Enter the