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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Enhancing Identification of Causal Effects by Pruning
Authors: Santtu Tikka, Juha Karvanen
JMLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present graphical criteria to detect variables which are redundant in identifying causal effects. We also provide an improved version of a well-known identifiability algorithm that implements these criteria. An introductory example motivates the use of the improved algorithm. We are interested in the causal effect of X on Y in graph G of Figure 1(a). Additional examples are provided as an R script (R Core Team, 2017) at the JMLR online paper repository. The script also includes all of the examples presented in this paper. |
| Researcher Affiliation | Academia | Santtu Tikka EMAIL Juha Karvanen EMAIL Department of Mathematics and Statistics P.O.Box 35 (Ma D) FI-40014 University of Jyvaskyla, Finland |
| Pseudocode | Yes | Algorithm 1 The causal effect of intervention do(X = x) on Y (ID). INPUT: Value assignments x and y, joint distribution P(v) and an SMG G = V, E . G is an I-map of P. OUTPUT: Expression for Px(y) in terms of P(v) or FAIL(F, F ). |
| Open Source Code | Yes | This algorithm is provided by the R package causaleffect which implements various causal inference algorithms such as the original ID algorithm (Tikka and Karvanen, 2017a). |
| Open Datasets | No | The paper focuses on theoretical advancements and algorithmic development, using illustrative causal graphs (e.g., Figure 1, Figure 2) rather than conducting experiments on specific datasets. Therefore, no datasets are mentioned as being publicly available. |
| Dataset Splits | No | The paper primarily presents theoretical criteria and an algorithm, illustrating its functionality with abstract causal graphs. It does not involve empirical experiments with datasets, and thus, no dataset splits are described. |
| Hardware Specification | No | The paper focuses on theoretical contributions and algorithmic development. It does not describe any computational experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper mentions the use of the 'R package causaleffect' and 'R script (R Core Team, 2017)'. However, it does not provide specific version numbers for R or the 'causaleffect' package, which is required for a reproducible description of ancillary software. |
| Experiment Setup | No | The paper focuses on theoretical criteria and an algorithm. It does not describe any experiments that would involve hyperparameter tuning or specific training configurations. |