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 | Conference PDF | Archive PDF | Plain Text | 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 tgao@us.ibm.com Dharmashankar Subramanian IBM Research dharmash@us.ibm.com Debarun Bhattacharjya IBM Research debarunb@us.ibm.com RPI shoux@rpi.edu Nicholas Mattei Tulane University nsmattei@tulane.edu Kristin Bennett RPI bennek@rpi.edu |
| 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. |