# RESIT¶

## Model¶

RESIT [2] is an estimation algorithm for Additive Noise Model [1].

This method makes the following assumptions.

1. Continouos variables
2. Nonlinearity
4. Acyclicity
5. No hidden common causes

References

 [1] P. O. Hoyer, D. Janzing, J. M. Mooij, and J. Peters. and B. Schölkopf. Nonlinear causal discovery with additive noise models. Advances in Neural Information Processing Systems 21, pages 689–696. 2009.
 [2] J. Peters, J. M. Mooij, D. Janzing, and B. Schölkopf. Causal discovery with continuous additive noise models Journal of Machine Learning Research, 15: 2009–2053, 2014.

## Import and settings¶

In this example, we need to import numpy, pandas, and graphviz in addition to lingam.

import numpy as np
import pandas as pd
import graphviz
import lingam
from lingam.utils import print_causal_directions, print_dagc, make_dot

import warnings
warnings.filterwarnings('ignore')

print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])

np.set_printoptions(precision=3, suppress=True)

['1.21.5', '1.3.2', '0.17', '1.6.0']


## Test data¶

First, we generate a causal structure with 7 variables. Then we create a dataset with 6 variables from x0 to x5, with x6 being the latent variable for x2 and x3.

X = pd.read_csv('nonlinear_data.csv')

m = np.array([
[0, 0, 0, 0, 0],
[1, 0, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 1, 1, 0, 0],
[0, 0, 0, 1, 0]])

dot = make_dot(m)

# Save pdf
dot.render('dag')

# Save png
dot.format = 'png'
dot.render('dag')

dot


## Causal Discovery¶

To run causal discovery, we create a RESIT object and call the fit method.

from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(max_depth=4, random_state=0)

model = lingam.RESIT(regressor=reg)
model.fit(X)

<lingam.resit.RESIT at 0x201a773c548>


Using the causal_order_ properties, we can see the causal ordering as a result of the causal discovery. x2 and x3, which have latent confounders as parents, are stored in a list without causal ordering.

model.causal_order_

[0, 1, 2, 3, 4]


Also, using the adjacency_matrix_ properties, we can see the adjacency matrix as a result of the causal discovery. The coefficients between variables with latent confounders are np.nan.

model.adjacency_matrix_

array([[0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[1., 1., 0., 0., 0.],
[0., 0., 0., 1., 0.]])


We can draw a causal graph by utility funciton.

make_dot(model.adjacency_matrix_)


## Bootstrapping¶

We call bootstrap() method instead of fit(). Here, the second argument specifies the number of bootstrap sampling.

import warnings
warnings.filterwarnings('ignore', category=UserWarning)

n_sampling = 100
model = lingam.RESIT(regressor=reg)
result = model.bootstrap(X, n_sampling=n_sampling)


## Causal Directions¶

Since BootstrapResult object is returned, we can get the ranking of the causal directions extracted by get_causal_direction_counts() method. In the following sample code, n_directions option is limited to the causal directions of the top 8 rankings, and min_causal_effect option is limited to causal directions with a coefficient of 0.01 or more.

cdc = result.get_causal_direction_counts(n_directions=8, min_causal_effect=0.01, split_by_causal_effect_sign=True)


We can check the result by utility function.

print_causal_directions(cdc, n_sampling)

x1 <--- x0 (b>0) (100.0%)
x2 <--- x1 (b>0) (71.0%)
x4 <--- x1 (b>0) (62.0%)
x2 <--- x0 (b>0) (62.0%)
x3 <--- x1 (b>0) (53.0%)
x3 <--- x4 (b>0) (52.0%)
x4 <--- x3 (b>0) (47.0%)
x3 <--- x0 (b>0) (44.0%)


## Directed Acyclic Graphs¶

Also, using the get_directed_acyclic_graph_counts() method, we can get the ranking of the DAGs extracted. In the following sample code, n_dags option is limited to the dags of the top 3 rankings, and min_causal_effect option is limited to causal directions with a coefficient of 0.01 or more.

dagc = result.get_directed_acyclic_graph_counts(n_dags=3, min_causal_effect=0.01, split_by_causal_effect_sign=True)


We can check the result by utility function.

print_dagc(dagc, n_sampling)

DAG[0]: 13.0%
x1 <--- x0 (b>0)
x2 <--- x1 (b>0)
x3 <--- x4 (b>0)
x4 <--- x0 (b>0)
x4 <--- x1 (b>0)
DAG[1]: 13.0%
x1 <--- x0 (b>0)
x2 <--- x0 (b>0)
x2 <--- x1 (b>0)
x3 <--- x4 (b>0)
x4 <--- x1 (b>0)
DAG[2]: 11.0%
x1 <--- x0 (b>0)
x2 <--- x1 (b>0)
x3 <--- x0 (b>0)
x3 <--- x1 (b>0)
x4 <--- x3 (b>0)


## Probability¶

Using the get_probabilities() method, we can get the probability of bootstrapping.

prob = result.get_probabilities(min_causal_effect=0.01)
print(prob)

[[0.   0.   0.   0.02 0.  ]
[1.   0.   0.07 0.05 0.01]
[0.62 0.71 0.   0.06 0.03]
[0.44 0.53 0.18 0.   0.52]
[0.43 0.62 0.21 0.47 0.  ]]


## Bootstrap Probability of Path¶

Using the get_paths() method, we can explore all paths from any variable to any variable and calculate the bootstrap probability for each path. The path will be output as an array of variable indices. For example, the array [0, 1, 3] shows the path from variable X0 through variable X1 to variable X3.

from_index = 0 # index of x0
to_index = 3 # index of x3

pd.DataFrame(result.get_paths(from_index, to_index))

path effect probability
0 [0, 1, 3] 1.0 0.53
1 [0, 1, 4, 3] 1.0 0.51
2 [0, 3] 1.0 0.44
3 [0, 4, 3] 1.0 0.33
4 [0, 2, 3] 1.0 0.12
5 [0, 1, 2, 3] 1.0 0.11
6 [0, 2, 4, 3] 1.0 0.07
7 [0, 1, 2, 4, 3] 1.0 0.04
8 [0, 1, 4, 2, 3] 1.0 0.03
9 [0, 2, 1, 3] 1.0 0.01
10 [0, 4, 1, 3] 1.0 0.01