Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Inferring Cause and Effect in the Presence of Heteroscedastic Noise
Authors: Sascha Xu, Osman A Mian, Alexander Marx, Jilles Vreeken
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide a thorough empirical evaluation on synthetic and real world data, comparing to a wide range of methods for bi-variate causal inference. These results show that our method, HECI, performs as well as the strongest competitor whenever noise is homoscedasctic, and is the strongest whenever noise does depend on the cause. In this section, we empirically evaluate HECI on both synthetic data and the real-world T ubingen cause and effect pairs (Mooij et al., 2016) benchmark. |
| Researcher Affiliation | Academia | 1CISPA Helmholtz Center for Information Security, Saarbr ucken, Germany 2ETH Z urich & ETH AI Center, Z urich, Switzerland. |
| Pseudocode | Yes | Algorithm 1: HECI(X, Y, ) |
| Open Source Code | Yes | HECI is implemented in Python and we provide the source code as well as the synthetic data for research purposes.1 https://eda.mmci.uni-saarland.de/heci/ |
| Open Datasets | Yes | We test HECI on two different settings. First, we generate synthetic data according to our assumed causal model in Eq. (1). Next, we use the synthetic data of Gaussian processes provided by Tagasovska et al. (2020) over different noise settings. Last, we benchmark on the real-world T ubingen Cause Effect pairs dataset. |
| Dataset Splits | No | The paper describes how synthetic data is generated and mentions using the Tübingen dataset, but it does not specify explicit train/validation/test dataset splits or cross-validation setups for model training and evaluation. |
| Hardware Specification | Yes | All experiments were executed on a 4-core Intel i7 machine with 16 GB RAM, running Windows 10. |
| Software Dependencies | No | The paper states |
| Experiment Setup | Yes | We initiate the binning algorithm with b equal-width bins that partition the domain of X. A local function is fitted inside a single bin or over multiple, neighboring bins. In our experiments, we set = 0.05, with which the best performance was achieved. We therefore require a min support of 10 unique data points per bin. To choose the polynomial degree, we use BIC and minimize |