Double Machine Learning for Causal Inference under Shared-State Interference
Authors: Chris Hays, Manish Raghavan
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In each instantiation, we provide simulations validating that our method produces estimators that concentrate around the true treatment effects in finite samples. We also show our consistent variance estimators can be used to construct confidence intervals with the desired coverage probability. |
| Researcher Affiliation | Academia | Chris Hays 1 Manish Raghavan 1 1MIT. Correspondence to: Chris Hays <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 DML for shared-state interference |
| Open Source Code | Yes | Full details of the simulations are available in Appendix F and the code used to generate them is available at github.com/johnchrishays/dml4ssi. |
| Open Datasets | No | In our simulations, we generated data according to a smooth function of both Dt, Xt and of Ht. For all machine learning predictors ˆf, ˆm and f , we used random forests with default parameters trained on auxiliary data of size T sampled independently of the data used for inference. |
| Dataset Splits | No | The paper generates synthetic data for simulations and does not use or describe standard training/test/validation splits for a fixed dataset. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware (e.g., GPU models, CPU types, cloud instances) used for running the experiments. |
| Software Dependencies | No | For all machine learning predictors ˆf, ˆm and f , we used random forests with default parameters trained on auxiliary data of size T sampled independently of the data used for inference. |
| Experiment Setup | No | The paper mentions using 'random forests with default parameters' for machine learning predictors, but does not provide specific hyperparameter values (e.g., learning rate, batch size) for these models. It provides parameters for the synthetic data generation and simulation length, but not model training hyperparameters. |