Counterfactual Realizability
Authors: Arvind Raghavan, Elias Bareinboim
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations in the online setting corroborate this finding. Fig. 6(c,d) shows the cumulative regret (CR) and optimal arm probability (OAP) over 2000 iterations averaged over 200 epochs (CI=95%). We adapt Thompson Sampling to implement the strategies in Table 1. Details of implementation are in App. F.3.1 here. |
| Researcher Affiliation | Academia | Arvind Raghavan and Elias Bareinboim Causal Artificial Intelligence Lab Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1 CTF-REALIZE 1: Input: L3-distribution Q = P(W ); causal diagram G; action set A 2: Output: I.i.d sample W(i) from Q; FAIL if Q is not realizable given G, A 3: Fix a topological ordering Top(G) 4: SELECT(i) for a new unit i 5: for V in order Top(G) do 6: INTV {Interventions for V } 7: OUTPUTV {Index in output vector} 8: for each term Wt in expression W do 9: if V An(W)GT and V = W then 10: Call COMPATIBLE(V, Wt) Alg. 2 11: end if 12: if V = W then 13: Add {Wt} to OUTPUTV 14: end if 15: end for |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code or a link to a code repository. It mentions 'Proofs and experiment details are in the full technical report (Raghavan & Bareinboim, 2025)' and 'Details of implementation are in App. F.3.1 here', referring to another document, but not source code. |
| Open Datasets | No | The paper uses simulated examples (Example 2: 'holdout set of fake CVs', Example 3: 'user of a social media platform'). It does not provide access information (links, DOIs, formal citations) for any publicly available datasets. |
| Dataset Splits | No | The paper mentions a 'holdout set of fake CVs' in Example 2 but does not specify any training/test/validation splits. Example 3 involves simulations over iterations and epochs, which is not dataset splitting in the traditional sense. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments, such as GPU models, CPU types, or cloud computing resources. |
| Software Dependencies | No | The paper mentions adapting 'Thompson Sampling' for implementation but does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow, specific libraries, or their versions). |
| Experiment Setup | No | The paper mentions '2000 iterations averaged over 200 epochs (CI=95%)' for simulations. However, it defers detailed experimental setup, such as specific hyperparameters or model initialization settings, to appendices of a separate technical report (e.g., 'Details of the SCM, latent confounders, and the optimal L3-strategy are in App. F.3 here.'). The main text does not contain these specific details. |