Identification of Conditional Causal Effects under Markov Equivalence

Authors: Amin Jaber, Jiji Zhang, Elias Bareinboim

NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this work, we derive an algorithm to identify conditional effects, which are particularly useful for evaluating conditional plans or policies. ... We show that the proposed algorithm subsumes the state-of-the-art algorithm in (Jaber et al., 2019a), which is complete for unconditional effects.
Researcher Affiliation Academia Amin Jaber Purdue University jaber0@purdue.edu; Jiji Zhang Lingnan University jijizhang@ln.edu.hk; Elias Bareinboim Columbia University eb@cs.columbia.edu
Pseudocode Yes Algorithm 1 IDP(x, y) given PAG P; Algorithm 2 Recursive routine to decompose Q[T|Z]; Algorithm 3 CIDP(x, y, z) given PAG P
Open Source Code No The paper does not provide any statement or link regarding the availability of open-source code for the described methodology.
Open Datasets No This is a theoretical paper focused on algorithm derivation and properties. It does not conduct empirical studies with datasets, therefore, no information on public dataset availability for training is provided.
Dataset Splits No This is a theoretical paper and does not involve empirical experiments. Therefore, no information on training/validation/test dataset splits is provided.
Hardware Specification No This is a theoretical paper and does not involve empirical experiments requiring hardware specifications. Therefore, no hardware details are provided.
Software Dependencies No This is a theoretical paper and does not involve empirical experiments requiring specific software dependencies with version numbers. Therefore, no such information is provided.
Experiment Setup No This is a theoretical paper and does not involve empirical experiments or specific hyperparameter tuning. Therefore, no experimental setup details are provided.