41 citations · 112 across the 21 of their papers we have counts for
7 papers · 1 filter
Invariance of Clustering Operations in Causal Effect Identification
Jani Nykänen, Otto Tabell, Santtu Tikka +1
Clustering variables in causal graphs reduces the size of the graph and simplifies causal inference. However, arbitrary clustering can alter crucial causal relations among variable…
Clustering and Pruning in Causal Data Fusion
Otto Tabell, Santtu Tikka, Juha Karvanen
Data fusion, the process of combining observational and experimental data, can enable the identification of causal effects that would otherwise remain non-identifiable. Although id…
Simulating counterfactuals
Juha Karvanen, Santtu Tikka, Matti Vihola
Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world. If the evidence specifies a conditional distrib…
Clustering and Structural Robustness in Causal Diagrams
Santtu Tikka, Jouni Helske, Juha Karvanen
Graphs are commonly used to represent and visualize causal relations. For a small number of variables, this approach provides a succinct and clear view of the scenario at hand. As…
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…
Enhancing Identification of Causal Effects by Pruning
Santtu Tikka, Juha Karvanen
Causal models communicate our assumptions about causes and effects in real-world phe- nomena. Often the interest lies in the identification of the effect of an action which means d…
Simplifying Probabilistic Expressions in Causal Inference
Santtu Tikka, Juha Karvanen
Obtaining a non-parametric expression for an interventional distribution is one of the most fundamental tasks in causal inference. Such an expression can be obtained for an identif…