Some of my talks


Flexible Bayesian modelling and causal inference for panel data with R package dynamite

Panel data, consisting of various measurements from multiple subjects followed over several time points, are commonly studied in social sciences and other fields. Such data can naturally be analyzed in various ways, depending on the research questions and the characteristics of the data. Popular, somewhat overlapping modelling approaches include dynamic panel models, fixed effect models, and variations of cross-lagged panel models. In this talk, I extend the traditional cross-lagged panel model to handle time-varying effects and non-Gaussian response variables and show how Bayesian posterior predictive distributions can be used to evaluate long-term counterfactual predictions which take into account the dynamic structure of the assumed causal graph of the system. Finally, I give an overview of a new R package dynamite for Bayesian inference for panel data.

December 2, 2022

Statistics Seminar at University of Jyväskylä / University of Jyväskylä, Department of Mathematics and Statistics

Slides code


bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R

State space models are a flexible class of latent variable models commonly used in analysing time series data. The R package bssm is designed for Bayesian inference of general state space models with non-Gaussian and/or non-linear observational and state equations. The package provides easy-to-use and efficient functions for fully Bayesian inference with common time series models such as basic structural time series model with exogenous covariates, simple stochastic volatility models, and discretized diffusion models, making it straightforward and efficient to make predictions and other inference in a Bayesian setting. Unlike the existing packages, bssm allows for easy-to-use approximate inference based on Gaussian approximations such as the Laplace approximation and the extended Kalman filter. The inference is based on fully automatic, adaptive Markov chain Monte Carlo (MCMC) on the hyperparameters, with optional parallelizable importance sampling post-correction to eliminate any approximation bias. The bssm package implements also a direct pseudo-marginal MCMC and a delayed acceptance pseudo-marginal MCMC using intermediate approximations. The package supports directly models with linear-Gaussian state dynamics with non-Gaussian observation models and has an Rcpp interface for specifying custom non-linear and diffusion models.

July 9, 2021

UseR!2021 conference / Session 9C, online UseR!2021 Conference

Slides code

Estimation of causal effects with small data in the presence of trapdoor variables

We consider the problem of estimating causal effects of interventions from observational data when well-known back-door and front-door adjustments are not applicable. We show that when an identifiable causal effect is subject to an implicit functional constraint that is not deducible from conditional independence relations, the estimator of the causal effect can exhibit bias in small samples (where parameter estimation exhibits non-negligible uncertainty). This bias is related to variables that we call trapdoor variables. We use simulated data to study different strategies to account for trapdoor variables and suggest how the related trapdoor bias might be minimized. The importance of trapdoor variables in causal effect estimation is illustrated with real data from the Life Course 1971-2002 study. Using this dataset, we estimate the causal effect of education on income in the Finnish context. Using the Bayesian modelling approach allows us to take the parameter uncertainty into account and gives us the full interventional distribution instead of only average causal effect estimates.


Bayesian reconstruction of historical population in Finland 1647-1850

The scarcity of long-run historical population series is a major problem because these are vital inputs for many fields of history, demography, and economics. Perhaps the most crucial omission thus far in historical population reconstructions has been the unavailability of uncertainty estimates, leaving the door open for conflicting interpretations of respective population developments. In this talk, I will describe a Bayesian hierarchical time series model that allows us to integrate partially observed parish level data and prior information in a coherent manner, providing us with model-based posterior intervals for the population estimates. We demonstrate its applicability by estimating long-term Finnish population development from 1647 onwards. This puts Finland among the very few countries with an annual population series of this length available.
(Slides start with a short bio)

October 2, 2019

Joint Statistics and DEMO seminar / Department of Mathematics and Statistics, University of Jyväskylä, Finland

Slides code