Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Identification of Conditional Causal Effects under Markov Equivalence
Authors: Amin Jaber, Jiji Zhang, Elias Bareinboim
NeurIPS 2019 | Venue PDF | 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 EMAIL; Jiji Zhang Lingnan University EMAIL; Elias Bareinboim Columbia University EMAIL |
| 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. |