Whassup, peeps! It’s been a minute since my last post (shoutout to the one person probably reading this, you the real MVP!).
My Master’s thesis, with some fresh updates, just dropped on arXiv. Check it:
Paper: Babasaki, K., Sugasawa, S., McAlinn, K. and Takanashi, K. (2024). Ensemble doubly robust Bayesian inference via regression synthesis. (arXiv:2409.06288)
So, this paper, it’s all about takin’ this ensemble method called Bayesian Predictive Synthesis (BPS) that Professor McAlinn cooked up, and flexing it into the world of causal inference, specifically for estimatin’ Average Treatment Effects (ATE). We’re callin’ our new method “doubly robust Bayesian regression synthesis”. If you wanna get into the nitty-gritty, peep the paper, ya dig?
For the real heads, check out these papers to level up your understanding before divin’ in:
- McAlinn, K. & West, M. (2019). Dynamic Bayesian Predictive Synthesis in Time Series Forecasting. (Journal of Econometrics 210: 155-169)
- Sugasawa, S., McAlinn, K., Takanashi, K. and Airoldi, E. A. (2023). Bayesian causal synthesis for meta-inference on heterogeneous treatment effect. (arXiv:2304.07726)
This paper came about cuz I took this dope econometrics seminar (taught by Professor McAlinn) at Keio’s economics department.
Don’t let his looks fool ya, Professor McAlinn is mad chill. So, if you’re a Keio grad student into Bayesian stats or causal inference, def sign up for his class.
Big ups to Professor Sugasawa, Professor McAlinn, and Professor Takanashi for their guidance on this paper. Mad respect!