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 Inference for Event Pairs in Multivariate Point Processes
Authors: Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Xiao Shou, Nicholas Mattei, Kristin P Bennett
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an experimental investigation using synthetic and real-world event datasets, where our proposed causal inference framework is shown to exhibit superior performance against a set of baseline pairwise causal association scores. |
| Researcher Affiliation | Collaboration | Tian Gao IBM Research EMAIL Dharmashankar Subramanian IBM Research EMAIL Debarun Bhattacharjya IBM Research EMAIL RPI EMAIL Nicholas Mattei Tulane University EMAIL Kristin Bennett RPI EMAIL |
| Pseudocode | Yes | Algorithm 1 Inverse Probability Weighting for Events |
| Open Source Code | No | The code will be released in Github. |
| Open Datasets | Yes | We begin by comparing the ATE estimation performance of our proposed IPTW methods on synthetic event datasets, generated using different parameters. [...] We also test our methods on the diabetes dataset [14] a real-world dataset which we process into events for meals, exercise activity, insulin dosage and changes in blood glucose measurements for 70 diabetes patients. |
| Dataset Splits | Yes | The dataset is split into 50%/50% training/test sets, and optimal window setting is determined on the training set, which is then deployed in the test set for evaluation. |
| Hardware Specification | Yes | All experiments are done on a machine with 2.9 GHz quad-core CPU. |
| Software Dependencies | No | The paper mentions using 'tick: A Python library' [6] and 'PGEM' [7] but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We generate 3 models with different numbers of events, randomly generated graph structures among events, fixed window size of w = 30, T = 2000, and random intensities between 0.1 and 0.4. We use the data and the generated model to obtain the true estimates of λy|Zt(t) at chosen times t and hence can compute the ground truth ATE. Since we observed that the sample size S of t (103 to 105) in the ATE estimation does not impact the results much, we use sample size S = 103 for all our experiments. |