36 citations · 109 across the 10 of their papers we have counts for
10 papers
Binary Independent Component Analysis: A Non-stationarity-based Approach
Antti Hyttinen, Vitória Barin-Pacela, Aapo Hyvärinen
We consider independent component analysis of binary data. While fundamental in practice, this case has been much less developed than ICA for continuous data. We start by assuming…
Towards Scalable Bayesian Learning of Causal DAGs
Jussi Viinikka, Antti Hyttinen, Johan Pensar +1
We give methods for Bayesian inference of directed acyclic graphs, DAGs, and the induced causal effects from passively observed complete data. Our methods build on a recent Markov…
Identifying Causal Effects via Context-specific Independence Relations
Santtu Tikka, Antti Hyttinen, Juha Karvanen
Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution in a given causal struct…
Do-search -- a tool for causal inference and study design with multiple data sources
Juha Karvanen, Santtu Tikka, Antti Hyttinen
Epidemiological evidence is based on multiple data sources including clinical trials, cohort studies, surveys, registries and expert opinions. Merging information from different so…
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach
Santtu Tikka, Antti Hyttinen, Juha Karvanen
Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributio…
Causal Discovery from Subsampled Time Series Data by Constraint Optimization
Antti Hyttinen, Sergey Plis, Matti Järvisalo +2
This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system…
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure
Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt +1
We present a very general approach to learning the structure of causal models based on d-separation constraints, obtained from any given set of overlapping passive observational or…
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables
Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
Much of scientific data is collected as randomized experiments intervening on some and observing other variables of interest. Quite often, a given phenomenon is investigated in sev…
Bayesian Discovery of Linear Acyclic Causal Models
Patrik O. Hoyer, Antti Hyttinen
Methods for automated discovery of causal relationships from non-interventional data have received much attention recently. A widely used and well understood model family is given…
Noisy-OR Models with Latent Confounding
Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
Given a set of experiments in which varying subsets of observed variables are subject to intervention, we consider the problem of identifiability of causal models exhibiting latent…