Identifiability of Causal Graphs under Non-Additive Conditionally Parametric Causal Models
Authors: Juraj Bodik, Valérie Chavez-Demoulin
JMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the empirical properties of our methodology on various datasets, demonstrating state-of-the-art performance across multiple benchmarks. Furthermore, we propose an algorithm for estimating the causal structure from random samples drawn from CPCM. |
| Researcher Affiliation | Academia | Juraj Bodik EMAIL HEC Lausanne University of Lausanne CH-1015 Lausanne, Switzerland Valerie Chavez-Demoulin EMAIL HEC Lausanne University of Lausanne CH-1015 Lausanne, Switzerland |
| Pseudocode | Yes | Algorithm 1: CPCM(F) bivariate case. Algorithm 2: RESIT-greedy algorithm. Algorithm 3: Sequential approach for the choice between S1 and S2. Algorithm 4: Regression with Subsequent Independence Test (RESIT; Peters et al. (2014)), modified for CPCM(F1, . . . , Fk). |
| Open Source Code | Yes | The R code for the presented algorithms, simulations, and application is available at https://github.com/jurobodik/Causal_CPCM.git. |
| Open Datasets | Yes | We consider seven benchmark datasets, described below. The first five datasets are taken directly from Tagasovska et al. (2020) and described in Appendix B.3. [...] We demonstrate the advantages of CPCM using a subset of the French MTPL motor insurance dataset (Sarpal, 2025). |
| Dataset Splits | No | The paper describes simulation setups with specific sample sizes (e.g., 'simulate 100 pairs with n = 1000 data points each' in Section 6.3, and 'focus on a subset of the first n = 1000 records' in Section 6.5), but does not explicitly detail training, testing, or validation splits for these datasets or for evaluation purposes. |
| Hardware Specification | Yes | Runtime was measured on a machine with an Intel Core i5-6300U 2.5 GHz processor and 16 GB of RAM. |
| Software Dependencies | No | The paper mentions using R, the mgcv package, bnlearn package, and pcalg package, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | If the p-value is larger than α = 0.05, mark X1 X2 as plausible. [...] With regard to choice of ρ, we use minus the logarithm of the p-value of the independence test (Genest et al., 2019) and λ = 2. |