We lifted 9 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| dongxinshuai/scm-identify | canonical | 8 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| extract_graph_edges | Ran | dongxinshuai/scm-identify/pytetrad/utils.py code served (permissive licence) · get_code("023237ccf04802f6") |
| extract_graph_nodes | Ran | dongxinshuai/scm-identify/pytetrad/utils.py code served (permissive licence) · get_code("571d798439a11843") |
| generate_adjacency_matrix_ordered | Ran | dongxinshuai/scm-identify/pytetrad/utils.py code served (permissive licence) · get_code("eb9db603347e0c5e") |
| get_cov_V_by_trek | Ran | dongxinshuai/scm-identify/ParameterLearning/linear_parameter_identification.py code served (permissive licence) · get_code("2c0303ade8044f0f") |
| graphs_to_probs | Ran | dongxinshuai/scm-identify/pytetrad/visualize.py code served (permissive licence) · get_code("06611de7ad2b51b0") |
| parameter_by_trek | Ran | dongxinshuai/scm-identify/ParameterLearning/linear_parameter_identification.py code served (permissive licence) · get_code("8b9e6f041fd74427") |
| tetrad_graph_to_pcalg | Ran | dongxinshuai/scm-identify/pytetrad/translate.py code served (permissive licence) · get_code("6868e44567fe9ad5") |
| write_gdot | Ran | dongxinshuai/scm-identify/pytetrad/visualize.py code served (permissive licence) · get_code("94192dfd38028e02") |
| parameter_standard | Not yet run | dongxinshuai/scm-identify/ParameterLearning/linear_parameter_identification.py code served (permissive licence) · get_code("8dae5f0ad0668279") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Linear causal models are important tools for modeling causal dependencies and yet in practice, only a subset of the variables can be observed. In this paper, we examine the parameter identifiability of these models by investigating whether the edge coefficients can be recovered given the causal structure and partially observed data. Our setting is more general than that of prior research - we allow all variables, including both observed and latent ones, to be flexibly related, and we consider the coefficients of all edges, whereas most existing works focus only on the edges between observed variables. Theoretically, we identify three types of indeterminacy for the parameters in partially observed linear causal models. We then provide graphical conditions that are sufficient for all parameters to be identifiable and show that some of them are provably necessary. Methodologically, we propose a novel likelihood-based parameter estimation method that addresses the variance indeterminacy of latent variables in a specific way and can asymptotically recover the underlying parameters up to trivial indeterminacy. Empirical studies on both synthetic and real-world datasets validate our identifiability theory and the effectiveness of the proposed method in the finite-sample regime. Code: https://github.com/dongxinshuai/scm-identify.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2407.16975")
get_code_for_paper("2407.16975")
have("2407.16975")
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