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straxxus/scm-mechanism-drift

Structural Causal Model Environment Pairs with Mechanism Drift Labels Paired-environment structural causal model (SCM) data with ground-truth labels for which structural mechanism changed between two environments — plus the deterministic generator that produces it. Fully synthetic. No external data of any kind: nothing downloaded, scraped, purchased, or derived from any existing corpus, dataset or benchmark. No large language model output appears in the data, the labels, the… See the full description on the dataset page: https://huggingface.co/datasets/straxxus/scm-mechanism-drift.

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Structural Causal Model Environment Pairs with Mechanism Drift Labels

Paired-environment structural causal model (SCM) data with ground-truth labels for which structural mechanism changed between two environments — plus the deterministic generator that produces it.

Fully synthetic. No external data of any kind: nothing downloaded, scraped, purchased, or derived from any existing corpus, dataset or benchmark. No large language model output appears in the data, the labels, the graphs or the identifiers. Every value is produced by numpy from a supplied seed.

What a case contains

—a random directed acyclic graph over 10 variables (at most 3 parents per node),
—a reference environment sample and a current environment sample,
—ground truth naming, for every node, whether its structural equation changed and which parameter changed: an incoming coefficient, the intercept, or the noise scale.

Each node follows

X_j = b_j + SUM_i ( W[j][i] * X_i ) + SUM_i ( nl[j][i] * tanh(X_i) ) + eps_j

Noise is Gaussian, or Student-t on a minority of nodes. Nonlinear parent terms and heavy tails are identical in both environments and are never interventions — they exist so the exact parametric family cannot simply be assumed.

Node labels v01–v10 are permuted independently per case, and columns are emitted in that permuted order, so node identity carries no information across cases.

Why the data is interesting

A changed mechanism and a changed distribution are not the same thing. When a node's structural equation is altered the change propagates downstream, so every descendant of an intervened node also shows a different marginal distribution while its own equation is untouched — on average about 4 such "pure descendants" per case.

Marginal two-sample testing therefore flags the innocent nodes. Recovering the intervened set requires reasoning conditionally on each node's parents. That gap is the point of the data.

Files

File	Rows	Description
`data/cases.csv`	600	one row per case
`data/observations.csv`	110,814	long format, one row per (case, environment, observation)
`data/generation_config.json`	—	every parameter used, including the seed for this instance

Join on case_id.

`cases.csv`

Column	Type	Description
`case_id`	string	opaque case identifier, `cm_` + 16 hex characters
`n_rows`	int	observations in each environment (50–140)
`graph_edges`	string	DAG as pipe-separated `parent>child`, e.g. `v03>v07\	v08>v02`
`drift_regime`	categorical	intervention magnitude: `clear`, `contested`, `knife_edge`
`shift_code`	string	label, 10 chars over `{N,C,I,Z}`; char i describes node `v(i+1)`
`n_shift`	int	number of intervened nodes (1–3)
`n_pure_descendants`	int	nodes downstream of an intervention that were not themselves intervened on
`ancestrally_coupled`	int	1 if one intervened node is a descendant of another

shift_code alphabet: N unchanged · C coefficient changed · I intercept changed · Z noise scale changed.

`observations.csv`

Column	Type	Description
`case_id`	string	joins to `cases.csv`
`environment`	categorical	`ref` or `cur`
`obs_index`	int	row index within the (case, environment) block
`v01` … `v10`	float	observed values, rounded to 4 decimals

Generating more data

bash

python generate_dataset.py --seed 12345 --cases 5200 --out ./my_data

--seed and --out are required; there is no default seed. Generation is fully deterministic: the same seed reproduces both CSVs byte for byte (verified by SHA-256). All randomness flows through numpy.random.default_rng — no global random state, no wall-clock, no network. Requires only numpy and pandas.

Scope of the included instance

The instance in data/ is a 600-case reference sample generated with the published seed `12345`, provided so the schema, distributions and data shape can be inspected directly.

This repository is the canonical source and generator for this dataset family. Larger instances — including any used for evaluation or benchmarking — are produced by the same generator under a different seed.

Evaluation instances deliberately do not publish their seed. The seed reproduces both the labels and the case_id values exactly, so publishing a generator together with an evaluation instance's seed would publish that instance's answer key. If you build a benchmark from this generator, keep its seed private.

Licence

CC0-1.0 (Creative Commons Zero v1.0 Universal) — public domain dedication, applying to everything in this repository: the generator code and the data. There is no second licence and no dual-licensing. Commercial use, redistribution and modification are unrestricted, with no attribution requirement. Full text in LICENSE.

Citation

Structural Causal Model Environment Pairs with Mechanism Drift Labels.
Original synthetic data and generator. CC0-1.0.