Efficiently Finding Conditional Instruments for Causal Inference
Authors: Benito van der Zander, Johannes Textor, Maciej Liskiewicz
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 benito@tcs.uni-luebeck.de Johannes Textor Utrecht University The Netherlands johannes.textor@gmx.de Maciej Li skiewicz University of L ubeck Germany liskiewi@tcs.uni-luebeck.de |
| 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. |