1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
Monotone Missing Data: A Blessing and a Curse
Santtu Tikka, Juha Karvanen
Monotone missingness is commonly encountered in practice when a missing measurement compels another measurement to be missing. Because of the simpler missing data pattern, monotone…
Multiple Imputation Guided by Full Law and Target Law Identifiability under the Missing Not At Random Assumption
Juha Karvanen, Santtu Tikka
The central challenges in missing data models concern the identifiability of two distributions: the target law and the full law. The target law refers to the joint distribution of…
Full Law Identification under Missing Data with Categorical Variables
Santtu Tikka, Juha Karvanen
Missing data may be disastrous for the identifiability of causal and statistical estimands. In graphical missing data models, colluders are dependence structures that have a specia…