1 code implementation • 27 Jun 2023 • Juha Karvanen, Santtu Tikka, Matti Vihola
Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world.
1 code implementation • 8 Nov 2021 • Santtu Tikka, Jouni Helske, Juha Karvanen
Graphs are commonly used to represent and visualize causal relations.
no code implementations • NeurIPS 2019 • Santtu Tikka, Antti Hyttinen, Juha Karvanen
We show that deciding causal effect non-identifiability is NP-hard in the presence of CSIs.
1 code implementation • 6 Mar 2020 • Jouni Helske, Santtu Tikka, Juha Karvanen
This bias is related to variables that we call trapdoor variables.
Methodology Computation
no code implementations • 4 Feb 2019 • 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 distributions and structural knowledge on the generating system.
no code implementations • 19 Jun 2018 • Santtu Tikka, Juha Karvanen
Obtaining a non-parametric expression for an interventional distribution is one of the most fundamental tasks in causal inference.
no code implementations • 19 Jun 2018 • Santtu Tikka, Juha Karvanen
Causal models communicate our assumptions about causes and effects in real-world phe- nomena.
no code implementations • 19 Jun 2018 • Santtu Tikka, Juha Karvanen
Identification of causal effects is one of the most fundamental tasks of causal inference.