Saber Salehkaleybar, Negar Kiyavash, Daniele Tramontano, Yaroslav Kivva, Mathias Drton
We lifted 4 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| danieletramontano/Causal-Effect-Identification-in-LiNGAM-Models-with-Latent-Confounders | — | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| count_lists | Ran | danieletramontano/Causal-Effect-Identification-in-LiNGAM-Models-with-Latent-Confounders/Estimation/src/python/methods.py code served (permissive licence) · get_code("4081a093bf96c499") |
| init_w_guess_ | Ran | danieletramontano/Causal-Effect-Identification-in-LiNGAM-Models-with-Latent-Confounders/Estimation/src/python/methods.py code served (permissive licence) · get_code("796d1c3c48ec169b") |
| intersection | Ran | danieletramontano/Causal-Effect-Identification-in-LiNGAM-Models-with-Latent-Confounders/Estimation/src/python/methods.py code served (permissive licence) · get_code("cf83a84f693a8da2") |
| graphical_rica | Not yet run | danieletramontano/Causal-Effect-Identification-in-LiNGAM-Models-with-Latent-Confounders/Estimation/src/python/methods.py code served (permissive licence) · get_code("9501e8050eac45e5") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the causal graph is known a priori, and when it is unknown. In both settings, we provide a complete graphical characterization of the identifiable direct or total causal effects among observed variables. Moreover, we propose efficient algorithms to certify the graphical conditions. Finally, we propose an adaptation of the reconstruction independent component analysis (RICA) algorithm that estimates the causal effects from the observational data given the causal graph. Experimental results show the effectiveness of the proposed method in estimating the causal effects.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.02049")
get_code_for_paper("2406.02049")
have("2406.02049")
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