Validating Causal Inference Methods
Authors: Harsh Parikh, Carlos Varjao, Louise Xu, Eric Tchetgen Tchetgen
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate Credence s ability to accurately assess the relative performance of causal estimation techniques in an extensive simulation study and two real-world data applications from Lalonde and Project STAR studies. |
| Researcher Affiliation | Collaboration | 1Duke University, Durham NC, USA 2Amazon.com, Seattle WA, USA 3The Wharton School, University of Pennsylvania, Philadelphia PA, USA. |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | We demonstrate Credence s ability to accurately assess the relative performance of causal estimation techniques in an extensive simulation study and two real-world data applications from Lalonde and Project STAR studies. |
| Dataset Splits | No | The paper describes training Credence on 'a single observed sample' or 'the observational component' of datasets, but does not specify explicit train/validation/test splits in percentages or sample counts for model training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions several software packages and libraries such as 'grf R package', 'Econ ML', and 'scikit-learn', but does not specify their version numbers. |
| Experiment Setup | Yes | For Quadratic DGP... (Figure 3(b)). (2) For the second one, we constraint both f(X) and g(X, T) to be equal to zero for all X and T. (3) Lastly, for the third one, we shrink both f(X) towards zero but constraint g(X, T) = 0.15(2T 1) to understand the sensitivity of different methods to unobserved confounding. |