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Large Causal Models on Time Series

This repository contains code and resources for testing Large Causal Models (LCMs). The aim is discovering temporal causal relationships in time-series datasets, using pretrained deep NN models. The LCM takes as input a temporal dataset $X \in \mathbb{R}^{N \times D}$ where $N$ is the sample size and $D$ the feature size (number of time-series). The output is a lagged adjacency tensor of shape $(N, N, \ell_\text{max})$ where $\ell_\text{max}$ is the hyperparemeter of the maximum assumed lag.

Installation

Install the dependencies from the requirements.txt file, either on your base environment or into an existing conda environment using

pip install -r requirements.txt

Pre-trained weights

Obtain the LCM weights from the following URLs and place them into the res folder:

  • deep_CI_12_3_fine_tuned_frozen_sim_82k_pre_joint_220k (210 MB): download link (external links will be available soon)
  • lcm_CI_RH_12_3_merged_290k (4.6 GB): download link (external links will be available soon)

Illustrative Example

1. Import Required Libraries

At first, import the necessary modules for data generation, model prediction, and result visualization:

from pathlib import Path
from utils.model_wrapper import Architecture_PL
from utils.cp_utils import set_seed, create_example_data, run_cp_and_parse_res
from utils.plotting_utils import plot_summary_from_pred

2. Generate Synthetic Data

We generate synthetic data with 1000 time samples, where each column represents a different time series. Data are Min-max normalized and random seed set to 42 for reproducibility.

set_seed(42)

df = create_example_data(n=1000)
variable_names = list(df.columns)

3. Load the Pretrained Model

Load the .ckpt pretrained model for causal prediction:

models_path = 'res'
model_name = 'lcm_CI_RH_12_3_merged_290k'

model = Architecture_PL.load_from_checkpoint(Path(models_path) / f"{model_name}.ckpt")

4. Perform Causal Discovery

Run run_cp_and_parse_res to perform causal discovery on the previous data. The max_lag parameter specifies the maximum time window size for analyzing causal relationships:

# Run causal discovery with a maximum lag of 2
pred = run_cp_and_parse_res(model_name, model=model, df=df, max_lag=2, seed=42)

The result is a lagged adjacency tensor of shape (N, N, max_lag) where:

  • N is the number of input time-series
  • pred[i, j, k] represents the probability that the j-th time-series at time t-(max_lag - k) causes the i-th time-series at time t.

5. Visualize the Results

The predicted causal relationships can be visualized using plot_summary_from_pred. The plt_thr parameter controls the density of the graph: higher values result in fewer edges being displayed.

plot_summary_from_pred(pred, variable_names, plt_thr=0.5)

In the resulting graph, an edge from time series A to B marked as t-1 means that time series A at time t-1 caused time series B at time t.

Output plot of the summary graph.

The above example can be found in simple_example.py

Limitations

We assume Causal Markov Condition and Faithfulness throughout. The following assumptions are also made:

  • Causal inference of up to 12 variables and 3 time lags: The models can handle inputs up to 12 variables and $\ell_\text{max}=1,2,3$.

  • No contemporaneous effects: All cause-effect pairs are assumed to occur with lag $\ell > 0$.

  • No unobserved confounders: The model assumes that there are no latent confounders (unobserved common causes) that could influence the relationships between the variables.

  • Causal Stationarity: The graph structure and noise distribution of the SCM does not change over time.

  • Time-series stationarity: The input time-series are assumed to be stationary.