Learning Adjustment Sets from Observational and Limited Experimental Data
Authors: Sofia Triantafillou, Greg Cooper9940-9948
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments, Simulations setup. We examined the performance of our method in three different settings:, Evaluation measures. We examined the performance of our algorithms in terms of their ability to improve causal effect estimation for the observational population:, Results: Fig 3 shows results for random SMCMs (top row), and for SMCMs where the covariates are known to be pre-treatment. FAS improves the estimation of PX(Y )(| | closer to zero, lower variance) compared to Dexp, particularly in cases in where the experimental data come from a selected population. |
| Researcher Affiliation | Academia | So fia Trianta fillou, Greg Cooper University of Pittsburgh, Department of Biomedical Informatics {sot16, gfc}@pitt.edu |
| Pseudocode | Yes | Algorithm 1: Find Adjustment Set (FAS), Algorithm 2: Score Exp, Algorithm 3: Selection BN |
| Open Source Code | No | The paper does not include any statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We used RCT data from the STOMP trial (Parker et al. 2013), which estimated the effect of statin use on myalgia. |
| Dataset Splits | No | The paper mentions using 'Observational data Dobs' and 'Experimental data Dexp' and describes their sample sizes (e.g., '10,000 samples' for Dobs), but it does not provide specific train/validation/test splits, percentages, or sample counts, nor does it reference standard predefined splits for these datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or specific computing environments) used to run its simulations or analyze the data. |
| Software Dependencies | No | The paper states 'For Learn BN, we used FGES, an optimized version of GES (Chickering 2002)', which names a software, but it does not provide a specific version number for FGES/GES or any other software dependencies. |
| Experiment Setup | Yes | We used niters=100. We simulated Dobs with 10,000 samples from DAGs with mean in-degree 2. Each DAG includes a pair X, Y where X causes Y , and 10 additional covariates: 6 observed and 4 latent. We used two types of DAGs: (i) random DAGs and (ii) DAGs where all the additional covariates are pre-treatment. Variables were discrete with 2-3 categories each and random parameter values P(X|Pa(X)). Selection bias was imposed by adding binary selection nodes Si and random parameters P(Si=1|Vi). |