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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Causal Effect Identification in Uncertain Causal Networks
Authors: Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian, Matthew Vowels, Jalal Etesami, Negar Kiyavash
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose efficient algorithms to approximate this problem and evaluate them against both real-world networks and randomly generated graphs. |
| Researcher Affiliation | Academia | Sina Akbari EPFL, Switzerland EMAIL; Fateme Jamshidi EPFL, Switzerland EMAIL; Ehsan Mokhtarian EPFL, Switzerland EMAIL; Matthew J. Vowels University of Lausanne, Switzerland EMAIL; Jalal Etesami TUM, Germany EMAIL; Negar Kiyavash EPFL, Switzerland EMAIL |
| Pseudocode | Yes | We first present a recursive approach for solving the edge ID problem in Section 4.1, described in Algorithm 1... Algorithm 2 Heuristic algorithm for edge ID... Algorithm 3 Maximal Hedge... Algorithm 4 Heuristic algorithm 2. |
| Open Source Code | Yes | To replicate our findings, our Python implementations are accessible via https://github.com/SinaAkbarii/Causal-Effect-Identification-in-Uncertain-Causal-Networks and https://github.com/matthewvowels1/NeurIPS_testbed_ADMGs. |
| Open Datasets | Yes | The first Psych (22 nodes & 70 directed edges) concerns the putative structure from a causal discovery algorithm Structural Agnostic Model [18] using data collected as part of the Health and Relationships Project [32]. The other three Barley (48 nodes & 84 directed edges), Water (32 nodes & 66 directed edges), and Alarm (37 nodes & 46 directed edges) come from the bnlearn python package [28]. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. For simulations, it describes generating ADMG structures and sampling edge probabilities, then running algorithms on these. For real-world graphs, it states applying algorithms to existing graph structures without mentioning data splits for evaluation. |
| Hardware Specification | Yes | All experiments were carried out on an Intel i9-9900K CPU running at 3.6GHz. |
| Software Dependencies | No | The paper mentions 'our Python implementations' and 'bnlearn python package [28]' but does not provide specific version numbers for Python or the bnlearn package, nor for any other key software components. |
| Experiment Setup | Yes | For a given number of vertices, we uniformly sample 50 ADMG structures... Edges for each of these 100 graphs are sampled with probability of log(n)/n... For each graph, we sample directed and bidirected edge probabilities pe uniformly between 0.51 and 1.0... The outcome Y is selected to be the last vertex in the topological ordering... If the runtime exceeds 3 minutes, we abort and log that the algorithm has failed to find a solution... Hyperparameter settings for the Structural Agnostic Model used to generate the putative (directed) structure for the Psych real-world dataset. Parameter Value Learning Rate 0.01 DAG Penalty True DAG Penalty Weight 0.05 Number of Runs 50 Train Epochs 3000 Test Epochs 800 Mixed Data True hlayers 2 dhlayers 2 lambda1 10 lambda2 0.001 dlr 0.001 linear False nh 20 dnh 200 |