Papers › On the Parameter Identifiability of Partially Observed Linear Causal Models

On the Parameter Identifiability of Partially Observed Linear Causal Models

24 Jul 2024arXiv:2407.16975archive 2025-07-28

Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang

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.

PaperPDFCodeCode Syntology ran

In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.

Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2407.16975")

Code

Syntology Ran 8 of 9 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 8 ran with no contract checked.

By repository: official repository: 9 samples from 1 repository, 8 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

dongxinshuai/scm-identify officialmentioned in paperApache-2.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

9 samples harvested; 8 ran; 0 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.

8ran
1unverified

Licence: 0 of the 9 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from dongxinshuai/scm-identify. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

extract_graph_edges dongxinshuai/scm-identify/pytetrad/utils.py official repository ran Apache-2.0 (permissive) · 023237ccf04802f6 · report
extract_graph_nodes dongxinshuai/scm-identify/pytetrad/utils.py official repository ran Apache-2.0 (permissive) · 571d798439a11843 · report
generate_adjacency_matrix_ordered dongxinshuai/scm-identify/pytetrad/utils.py official repository ran Apache-2.0 (permissive) · eb9db603347e0c5e · report
get_cov_V_by_trek dongxinshuai/scm-identify/ParameterLearning/linear_parameter_identification.py official repository ran Apache-2.0 (permissive) · 2c0303ade8044f0f · report
graphs_to_probs dongxinshuai/scm-identify/pytetrad/visualize.py official repository ran Apache-2.0 (permissive) · 06611de7ad2b51b0 · report
parameter_by_trek dongxinshuai/scm-identify/ParameterLearning/linear_parameter_identification.py official repository ran Apache-2.0 (permissive) · 8b9e6f041fd74427 · report
tetrad_graph_to_pcalg dongxinshuai/scm-identify/pytetrad/translate.py official repository ran Apache-2.0 (permissive) · 6868e44567fe9ad5 · report
write_gdot dongxinshuai/scm-identify/pytetrad/visualize.py official repository ran Apache-2.0 (permissive) · 94192dfd38028e02 · report
parameter_standard dongxinshuai/scm-identify/ParameterLearning/linear_parameter_identification.py official repository unverified Apache-2.0 (permissive) · 8dae5f0ad0668279 · report

Tasks

parameter estimation

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

Focus

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections