Publications by YoungStatS
Heterogeneous Treatment Effects with Instrumental Variables: A Causal Machine Learning Approach
Problem Setting 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 situations, ranging in complexity...
9272 sym 4 img
Inclusion Process and Sticky Brownian Motions
Inclusion Process and Sticky Brownian Motions The ninth “One World webinar” organized by YoungStatS will take place on February 9th, 2022. Inclusion process (IP) is a stochastic lattice gas where particles perform random walks subjected to mutual attraction. For the inclusion process in the condensation regime one can extract that the scalin...
1986 sym 2 img
Reconciling the Gaussian and Whittle Likelihood with an application to estimation in the frequency domain
Overview Suppose \(\{X_t: t\in \mathbb{Z}\}\) is a second order stationary time series where \(c(r) = \text{cov}(X_{t+r},X_t)\) and \(f(\omega) = \sum_{r\in\mathbb{Z}}c(r)e^{ir\omega}\) are the corresponding autocovariance and spectral density function, respectively. For notational convenience, we assume the time series is centered, that is \(\te...
10373 sym 6 img
Universal estimation with Maximum Mean Discrepancy (MMD)
This is an updated version of a blog post on RIKEN AIP Approximate Bayesian Inference team webpage: https://team-approx-bayes.github.io/blog/mmd/ INTRODUCTION A very old and yet very exciting problem in statistics is the definition of a universal estimator \(\hat{\theta}\). An estimation procedure that would work all the time. Close your eyes, p...
15228 sym 2 img
Measuring dependence in the Wasserstein distance for Bayesian nonparametric models
Overview Bayesian nonparametric (BNP) models are a prominent tool for performing flexible inference with a natural quantification of uncertainty. Traditionallly, flexible inference within a homogeneous sample is performed with exchangeable models of the type \(X_1,\dots, X_n|\tilde \mu \sim T(\tilde \mu)\), where \(\tilde \mu\) is a random measur...
14096 sym 4 img
Recent Advancements in Applied Instrumental Variable Methods
Recent Advancements in Applied Instrumental Variable Methods Instrumental variables (IV) is one of most important and widespread research designs in economics and statistics, as it can identify causal effects in the presence of unobserved confounding. Over the past 30 years the science of IV has advanced considerably, in part through the contrib...
1913 sym 2 img
Recent Advances in Approximate Bayesian Inference
Recent Advances in Approximate Bayesian Inference In approximate Bayesian computation, likelihood function is intractable and needs to be itself estimated using forward simulations of the statistical model (Beaumont et al., 2002; Marin et al., 2012; Sisson et al., 2019; Martin et al., 2020). Recent years have seen numerous advances in approximat...
1622 sym 2 img
Selection of Priors in Bayesian Structural Equation Modeling
Selection of Priors in Bayesian Structural Equation Modelling Structural equation modeling (SEM) is an important framework within the social sciences that encompasses a wide variety of statistical models. Traditionally, estimation of SEMs has relied on maximum likelihood. Unfortunately, there also exist a variety of situations in which maximum l...
1695 sym 2 img