Geometry-Aware Instrumental Variable Regression
Authors: Heiner Kremer, Bernhard Schölkopf
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that SMM is competitive with state-of-the-art IV estimators in standard settings and can provide an improvement in presence of corrupted data and adversarial attacks. |
| Researcher Affiliation | Academia | Heiner Kremer 1 Bernhard Sch olkopf 1 1Max Planck Institute for Intelligent Systems. Correspondence to: Heiner Kremer <heiner.kremer@gmail.com>. |
| Pseudocode | Yes | Algorithm 1 n-stage Kernel-SMM Input: Initial function f, hyperparameters ϵ, λ, γx for i = 1, . . . , n do Compute Q( f) while not converged do f Gradient Descent(f, f RQ( f)(f)) end while f f end for Output: Function estimate f |
| Open Source Code | Yes | Implementations of our estimators are available at https: //github.com/Heiner Kremer/sinkhorn-iv/. |
| Open Datasets | No | We consider the Simple IV experiment of Bennett & Kallus (2023) with the following data generating process, Z = sin(πZ0/10) (11) T = 0.75Z0 + 3.5H + 0.14η 0.6 Y = f(T; θ0) 10U + 0.1η2 where η1, η2, U N(0, I) and Z0 Uniform([ 5, 5]). |
| Dataset Splits | No | We pick the best hyperparameter configuration by evaluating the MMR objective (Zhang et al., 2023) on a validation data set of the same size as the training set. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper mentions software components and methods like "radial basis function kernel," "limited memory BFGS method," and "optimistic Adam optimizer," but it does not specify version numbers for any of these software dependencies. |
| Experiment Setup | Yes | For SMM we choose the hyperparameters from the grid defined by ϵ [10 6, 10 4, 10 2] and λ/ϵ [10 6, 10 4, 10 2, 1.0]." and "We tuned the learning rates, for the model and adversary by evaluating the Deep GMM estimator for different values and fix them both to 5e 4 for all methods. In the same way we fix the batch size to 200 and the number of epochs to 3000. |