webinars

Distribution generalization and causal inference

2023-02-28

Distribution generalization and causal inference Monday, March 20th, 2023, 7:00 PT / 10:00 ET / 15:00 CET 1st joint webinar of the IMS New Researchers Group, Young Data Science Researcher Seminar Zürich and the YoungStatS Project. When & Where: […] Speakers: […] Abstract:…

machine-learning

Inference on Adaptively Collected Data

Ruohan Zhan / 2022-10-11

It is increasingly common for data to be collected adaptively, where experimental costs are reduced progressively by assigning promising treatments more frequently. However, adaptivity also poses great challenges on post-experiment inference, since observations are dependent, and standard estimates…

optimal-transport

Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport

Tin Lok James Ng / 2022-09-19

A NHPP defined on \({\cal S} \subset \mathbb{R}^{d}\) can be fully characterized through its intensity function \(\lambda: {\cal S} \rightarrow [0, \infty)\). We present a general model for the intensity function of a non-homogeneous Poisson process using measure transport. The model finds its roots…

estimation

Minimax Estimation and Identity Testing of Markov Chains

Geoffrey Wolfer / 2022-09-18

We briefly review the two classical problems of distribution estimation and identity testing (in the context of property testing), then propose to extend them to a Markovian setting. We will see that the sample complexity depends not only on the number of states, but also on the stationary and…

webinars

Theory and Methods for Inference in Multi-armed Bandit Problems

2022-04-19

Theory and Methods for Inference in Multi-armed Bandit Problems Multi-armed bandit (MAB) algorithms have been argued for decades as useful to conduct adaptively-randomized experiments. By skewing the allocation of the arms towards the more efficient or informative ones, they have the potential to…

causal-inference

Heterogeneous Treatment Effects with Instrumental Variables: A Causal Machine Learning Approach

Falco J. Bargagli Stoffi / 2021-12-06

In our forthcoming paper on Annals of Applied Statistics, we propose a new method – which we call Bayesian Causal Forest with Instrumental Variable (BCF-IV) – to interpretably discover the subgroups with the largest or smallest causal effects in an instrumental variable setting. These are many…