Effect-Invariant Mechanisms for Policy Generalization
Authors: Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan Murphy, Jonas Peters
JMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present empirical results using simulated data and a mobile health intervention dataset to demonstrate the effectiveness of our approach. Keywords: distribution generalization, policy learning, invariance, causality, domain adaptation |
| Researcher Affiliation | Academia | Sorawit Saengkyongam EMAIL Seminar for Statistics ETH Zürich Zürich, Switzerland Niklas Pfister EMAIL Department of Mathematical Sciences University of Copenhagen Copenhagen, Denmark Predrag Klasnja EMAIL School of Information University of Michigan Ann Arbor, MI, USA Susan Murphy EMAIL Department of Statistics Department of Computer Science Harvard University Cambridge, MA, USA Jonas Peters EMAIL Seminar for Statistics ETH Zürich Zürich, Switzerland |
| Pseudocode | Yes | Algorithm 1 (Wald e-invariance test) Algorithm 2 (DR-learner e-invariance test) |
| Open Source Code | Yes | The code for all experiments is available at https://github.com/sorawitj/effect-invariance. |
| Open Datasets | Yes | We apply our proposed approach to the study of a mobile health intervention for promoting physical activity called Heart Steps V1 (Klasnja et al., 2019). |
| Dataset Splits | Yes | Specifically, we first choose e E as a test environment (user) and split the dataset D into the test set Dtst := {(Xtst i , T tst i , Y tst i , etst i )}ntst i=1 and the training set Dtr := {(Xtr i , T tr i , Y tr i , etr i )}ntr i=1, where etst i = e and etr i Etr := E \ {e} for all i. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or memory) are provided in the paper for the experiments performed. |
| Software Dependencies | No | The paper mentions software like 'R-learner', 'econml Python package', and 'random forest', but does not provide specific version numbers for any of these components. |
| Experiment Setup | Yes | We generate datasets of size n {1000, 2000, 4000, 8000} according to the SCM in Example 1 with two training environments Etr = {0, 1}. Each of the noise variables (ϵU1, ϵU2, ϵX1, ϵX2, ϵX3, ϵT , ϵY ) is independently drawn from a standard Gaussian distribution. The environment-specific parameters (γ1 e, γ2 e, γ3 e, µe) are drawn independently from a uniform distribution on [−3, 3]. As for the initial policy, we consider a policy that depends on the full covariate set {X1, X2, X3}. More precisely, for all x X, the initial policy πtr selects a treatment according to πtr(T = 1 | X = x) = 1/(1 + e^(0.5+x1 −0.5x2+0.3x3)). |