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].
Simplifying Probabilistic Expressions in Causal Inference
Authors: Santtu Tikka, Juha Karvanen
JMLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an automatic simplification algorithm that seeks to eliminate symbolically unnecessary variables from these expressions by taking advantage of the structure of the underlying graphical model. Our method is applicable to all causal effect formulas and is readily available in the R package causaleffect. Algorithm 1 Simplification of an atomic expression A = T, S given graph G and topological ordering π. |
| 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 Simplification of an atomic expression A = T, S given graph G and topological ordering π. Algorithm 2 Construction of the joint distribution of the set J and a variable V given their conditional sets D and C using d-separation criteria in G. Algorithm 3 Insertion of variable M into the joint term P(J|D) using d-separation criteria in G. |
| Open Source Code | Yes | Our method is applicable to all causal effect formulas and is readily available in the R package causaleffect. We have also updated the causaleffect R-package to automatically apply these simplification procedures to causal effect expressions. |
| Open Datasets | No | The paper focuses on symbolic derivations and algorithms for simplifying probabilistic expressions in causal inference, using theoretical graphical models as examples (e.g., Figure 1, Figure 2). It does not involve empirical evaluation on external datasets. |
| Dataset Splits | No | The paper describes theoretical algorithms and symbolic manipulations using example graphical models. It does not involve empirical experiments with datasets, and therefore, no dataset splits are discussed. |
| Hardware Specification | No | The paper describes theoretical algorithms and symbolic manipulations, focusing on the conceptual development and mathematical proofs rather than empirical experimental results. Therefore, no specific hardware specifications are mentioned for running experiments. |
| Software Dependencies | No | Our method is applicable to all causal effect formulas and is readily available in the R package causaleffect. While the R package causaleffect is mentioned as the implementation platform, no specific version number for R or the causaleffect package itself is provided in the paper text. |
| Experiment Setup | No | The paper introduces a symbolic simplification algorithm and presents its theoretical foundations and proofs. The examples provided illustrate the step-by-step application of the algorithms on theoretical graphical models and expressions, rather than detailing an experimental setup with specific hyperparameters or training configurations. |