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..
Efficiently Finding Conditional Instruments for Causal Inference
Authors: Benito van der Zander, Johannes Textor, Maciej Liskiewicz
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Here we address two issues with conditional IVs. First, they are conceptually rather distant to standard IVs; even variables that are independent of X could qualify as conditional IVs. We propose a new concept called ancestral IV, which interpolates between the two existing notions. Second, so far only exponential-time algorithms are known to find conditional IVs in a given causal diagram. Indeed, we prove that this problem is NP-hard. Nevertheless, we show that whenever a conditional IV exists, so does an ancestral IV, and ancestral IVs can be found in polynomial time. |
| Researcher Affiliation | Academia | Benito van der Zander University of L ubeck Germany EMAIL Johannes Textor Utrecht University The Netherlands EMAIL Maciej Li skiewicz University of L ubeck Germany EMAIL |
| Pseudocode | Yes | function NEAREST-SEPARATOR(G, Y, Z) [...] function ANCESTRAL-INSTRUMENT(G, X, Y, Z) |
| Open Source Code | No | Our algorithms are implemented in the open-source software DAGitty [Textor et al., 2011]. (This refers to existing software that implements their algorithms, not a release of new source code specifically for the paper's methodology.) |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets. Therefore, it does not provide access information for a dataset used for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets. Therefore, it does not discuss training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithms and proofs rather than empirical experiments, so it does not specify any hardware used. |
| Software Dependencies | No | The paper mentions "DAGitty [Textor et al., 2011]" as open-source software where their algorithms are implemented, but it does not specify version numbers for DAGitty or any other software dependencies required to replicate the theoretical work or algorithms presented. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithms and proofs rather than empirical experiments. Thus, it does not provide details about an experimental setup, hyperparameters, or training settings. |