41 citations · 112 across the 21 of their papers we have counts for
8 papers · 1 filter
What Should We Measure Next? Finding Identification Strategies by Refining Mechanisms
Mikko Väänänen, Fanyu Cui, Carlos Cinelli +1
Canonical approaches in causal inference treat model specification as fixed, assuming that researchers directly translate all relevant domain knowledge into a causal model, which c…
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…
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…
dynamite: An R Package for Dynamic Multivariate Panel Models
Santtu Tikka, Jouni Helske
dynamite is an R package for Bayesian inference of intensive panel (time series) data comprising multiple measurements per multiple individuals measured in time. The package suppor…
Identifying Counterfactual Queries with the R Package cfid
Santtu Tikka
In the framework of structural causal models, counterfactual queries describe events that concern multiple alternative states of the system under study. Counterfactual queries ofte…
Simulation Framework for Realistic Large-scale Individual-level Data Generation with an Application in the Health Domain
Santtu Tikka, Jussi Hakanen, Mirka Saarela +1
We propose a framework for realistic data generation and simulation of complex systems and demonstrate its capabilities in the health domain. The main use cases of the framework ar…
Estimation of causal effects with small data in the presence of trapdoor variables
Jouni Helske, Santtu Tikka, Juha Karvanen
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…
Identifying Causal Effects with the R Package causaleffect
Santtu Tikka, Juha Karvanen
Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure.…