Fairness in ICML 2019 : “Optimal Decision Making under Strategic Behaviour”

Imagine one day, a huge number of decisions are made by machines. What would bother you?

Fairness in ICML 2019: “Learning Optimal Fair Policies”

I categorize Fairness in Machine Learning in two forms: 1) Static; 2) dynamic. The topics of static fairness are about prediction and classification. The topics of dynamic fairness are more about the policy of decision making and the feedback/impact of the decision. The difference is whether the algorithm considers the impact or feedback of the decision.

Confounding in Causality

Recently, I started reading papers about the confounding problem in causal inference. In general, it is impossible to get over the influence of confounding if only a treatment and an outcome are available. However, that is the beauty of research. And let’s how far we can go and where the state-of-art research is now.

I will start with two papers: 

1. “On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives”
2. “Causal regularization”