Blog

Learn practical lessons from a Kaggle competition on AI model runtime prediction. Explore the problem, prioritize data over the model, and embrace discomfort for personal growth.

Notes on the Fourth Chapter of "What If?": Effect Modification. Discusses how to detect and adjust for effect modifiers

Notes on the Third Chapter of "What If?": Observational Studies. Covers the move to causal inference on observed data, identifiability conditions, and the necessity of a target trial.

Discussing the foundations of causal estimation and its roots in Randomized Trials.

Working through the introductory chapter of "What If?" by Hernan and Robins.

A discussion of how to use neural networks as a rollout policy in Monte Carlo Tree Search, and how to use MCTS to train the neural network.

A brief introduction to Monte Carlo tree search, a powerful reinforcement learning technique that's been employed in some of the most revolutionary game playing AI, including AlphaGo and AlphaZero.