Causal Bounds in Quasi-Markovian Graphs
Authors: Madhumitha Shridharan, Garud Iyengar
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our heuristic on queries for the quasi-markovian graphs in Figure 1g, 1h and 1j in Zaffalon et al. (2020a). ... Overall, we evaluate our heuristic on 9 causal inference problems. ... In Sections 5.2.2 and 5.2.1, we benchmark Algorithm 1 against the most recent alternatives proposed in literature to our knowledge: both variations of Approx LP and the branch-and-bound algorithm in Duarte et al. (2021). |
| Researcher Affiliation | Academia | 1Department of Industrial Engineering and Operations Research, Columbia University, New York, USA. |
| Pseudocode | Yes | Algorithm 1 Frank Wolfe Heuristic for Lower Bound |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing source code for the described methodology or a direct link to a code repository. |
| Open Datasets | No | We evaluate our heuristic on queries for the quasi-markovian graphs in Figure 1g, 1h and 1j in Zaffalon et al. (2020a). ... For each problem, we randomly generate 50 values of q as our ground truth. Then for each value, we compute the corresponding true value of the query, and input data distribution P(VCk|VPCk ), k = 1, . . . , m. The paper references graph structures from a previous work but does not provide concrete access or citation details for the generated input data distributions used in their experiments. |
| Dataset Splits | No | The paper describes generating 'ground truth' values and running the heuristic multiple times (T=10 random restarts) but does not specify explicit training, validation, or test dataset splits in the conventional sense for model evaluation. |
| Hardware Specification | No | The paper mentions optimization solvers like Gurobi and SCIP, but does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | Yes | using readily available optimization solvers like Gurobi (Gurobi Optimization, LLC, 2023). |
| Experiment Setup | Yes | The procedure is initialized by generating a random feasible solution q(0). ... The entire procedure is repeated T times, each with a random initialization of the first iterate q(0). ... We then run Algorithm 1 on the input data distribution with T = 10, and check if the output bounds contain the true query value. |