# MultiGroupDirectLiNGAM¶

## Model¶

This algorithm [3] simultaneously analyzes multiple datasets obtained from different sources, e.g., from groups of different ages. The algorithm is an extention of DirectLiNGAM [1] to multiple-group cases. The algorithm assumes that each dataset comes from a basic LiNGAM model [2], i.e., makes the following assumptions in each dataset:

1. Linearity
2. Non-Gaussian continuous error variables (except at most one)
3. Acyclicity
4. No hidden common causes

Further, it assumes the topological causal orders are common to the groups. The similarity in the topological causal orders would give a better performance than analyzing each dataset separatly if the assumption on the causal orders are reasonable.

References

 [1] S. Shimizu, T. Inazumi, Y. Sogawa, A. Hyvärinen, Y. Kawahara, T. Washio, P. O. Hoyer and K. Bollen. DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model. Journal of Machine Learning Research, 12(Apr): 1225–1248, 2011.
 [2] S. Shimizu, P. O. Hoyer, A. Hyvärinen, and A. J. Kerminen. A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7:2003-2030, 2006.
 [3] S. Shimizu. Joint estimation of linear non-Gaussian acyclic models. Neurocomputing, 81: 104-107, 2012.

## 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

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

np.set_printoptions(precision=3, suppress=True)
np.random.seed(0)

['1.16.2', '0.24.2', '0.11.1', '1.5.4']


## Test data¶

We generate two datasets consisting of 6 variables.

x3 = np.random.uniform(size=1000)
x0 = 3.0*x3 + np.random.uniform(size=1000)
x2 = 6.0*x3 + np.random.uniform(size=1000)
x1 = 3.0*x0 + 2.0*x2 + np.random.uniform(size=1000)
x5 = 4.0*x0 + np.random.uniform(size=1000)
x4 = 8.0*x0 - 1.0*x2 + np.random.uniform(size=1000)
X1 = pd.DataFrame(np.array([x0, x1, x2, x3, x4, x5]).T ,columns=['x0', 'x1', 'x2', 'x3', 'x4', 'x5'])

x0 x1 x2 x3 x4 x5
0 2.239321 15.340724 4.104399 0.548814 14.176947 9.249925
1 2.155632 16.630954 4.767220 0.715189 12.775458 9.189045
2 2.284116 15.910406 4.139736 0.602763 14.201794 9.273880
3 2.343420 14.921457 3.519820 0.544883 15.580067 9.723392
4 1.314940 11.055176 3.146972 0.423655 7.604743 5.312976
m = np.array([[0.0, 0.0, 0.0, 3.0, 0.0, 0.0],
[3.0, 0.0, 2.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 6.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[8.0, 0.0,-1.0, 0.0, 0.0, 0.0],
[4.0, 0.0, 0.0, 0.0, 0.0, 0.0]])

make_dot(m)

x3 = np.random.uniform(size=1000)
x0 = 3.5*x3 + np.random.uniform(size=1000)
x2 = 6.5*x3 + np.random.uniform(size=1000)
x1 = 3.5*x0 + 2.5*x2 + np.random.uniform(size=1000)
x5 = 4.5*x0 + np.random.uniform(size=1000)
x4 = 8.5*x0 - 1.5*x2 + np.random.uniform(size=1000)
X2 = pd.DataFrame(np.array([x0, x1, x2, x3, x4, x5]).T ,columns=['x0', 'x1', 'x2', 'x3', 'x4', 'x5'])

x0 x1 x2 x3 x4 x5
0 1.913337 14.568170 2.893918 0.374794 12.115455 9.358286
1 2.013935 15.857260 3.163377 0.428686 12.657021 9.242911
2 3.172835 24.734385 5.142203 0.683057 19.605722 14.666783
3 2.990395 20.878961 4.113485 0.600948 19.452091 13.494380
4 0.248702 2.268163 0.532419 0.070483 1.854870 1.130948

m = np.array([[0.0, 0.0, 0.0, 3.5, 0.0, 0.0],
[3.5, 0.0, 2.5, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 6.5, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[8.5, 0.0,-1.5, 0.0, 0.0, 0.0],
[4.5, 0.0, 0.0, 0.0, 0.0, 0.0]])

make_dot(m)


We create a list variable that contains two datasets.

X_list = [X1, X2]


## Causal Discovery¶

To run causal discovery for multiple datasets, we create a MultiGroupDirectLiNGAM object and call the fit() method.

model = lingam.MultiGroupDirectLiNGAM()
model.fit(X_list)

<lingam.multi_group_direct_lingam.MultiGroupDirectLiNGAM at 0x21f895d0f60>


Using the causal_order_ properties, we can see the causal ordering as a result of the causal discovery.

model.causal_order_

[3, 0, 5, 2, 1, 4]


Also, using the adjacency_matrix_ properties, we can see the adjacency matrix as a result of the causal discovery. As you can see from the following, DAG in each dataset is correctly estimated.

print(model.adjacency_matrices_[0])

[[0.    0.    0.    3.006 0.    0.   ]
[2.873 0.    1.969 0.    0.    0.   ]
[0.    0.    0.    5.882 0.    0.   ]
[0.    0.    0.    0.    0.    0.   ]
[6.095 0.    0.    0.    0.    0.   ]
[3.967 0.    0.    0.    0.    0.   ]]

print(model.adjacency_matrices_[1])

[[ 0.     0.     0.     3.483  0.     0.   ]
[ 3.516  0.     2.466  0.165  0.     0.   ]
[ 0.     0.     0.     6.383  0.     0.   ]
[ 0.     0.     0.     0.     0.     0.   ]
[ 8.456  0.    -1.471  0.     0.     0.   ]
[ 4.446  0.     0.     0.     0.     0.   ]]


To compare, we run DirectLiNGAM with single dataset concatenating two datasets.

X_all = pd.concat([X1, X2])
print(X_all.shape)

(2000, 6)

model_all = lingam.DirectLiNGAM()
model_all.fit(X_all)

model_all.causal_order_

[1, 5, 2, 3, 0, 4]


You can see that the causal structure cannot be estimated correctly for a single dataset.

make_dot(model_all.adjacency_matrix_)


## Independence between error variables¶

To check if the LiNGAM assumption is broken, we can get p-values of independence between error variables. The value in the i-th row and j-th column of the obtained matrix shows the p-value of the independence of the error variables $$e_i$$ and $$e_j$$.

p_values = model.get_error_independence_p_values(X_list)
print(p_values[0])

[[0.    0.136 0.075 0.838 0.    0.832]
[0.136 0.    0.008 0.    0.544 0.403]
[0.075 0.008 0.    0.11  0.    0.511]
[0.838 0.    0.11  0.    0.039 0.049]
[0.    0.544 0.    0.039 0.    0.101]
[0.832 0.403 0.511 0.049 0.101 0.   ]]

print(p_values[1])

[[0.    0.545 0.908 0.285 0.525 0.728]
[0.545 0.    0.84  0.814 0.086 0.297]
[0.908 0.84  0.    0.032 0.328 0.026]
[0.285 0.814 0.032 0.    0.904 0.   ]
[0.525 0.086 0.328 0.904 0.    0.237]
[0.728 0.297 0.026 0.    0.237 0.   ]]


## Bootstrapping¶

In MultiGroupDirectLiNGAM, bootstrap can be executed in the same way as normal DirectLiNGAM.

results = model.bootstrap(X_list, n_sampling=100)


## Causal Directions¶

The bootstrap() method returns a list of multiple BootstrapResult, so we can get the result of bootstrapping from the list. We can get the same number of results as the number of datasets, so we specify an index when we access the results. We can get the ranking of the causal directions extracted by get_causal_direction_counts().

cdc = results[0].get_causal_direction_counts(n_directions=8, min_causal_effect=0.01)
print_causal_directions(cdc, 100)

x0 <--- x3  (100.0%)
x1 <--- x0  (100.0%)
x1 <--- x2  (100.0%)
x2 <--- x3  (100.0%)
x4 <--- x0  (100.0%)
x5 <--- x0  (100.0%)
x4 <--- x2  (94.0%)
x4 <--- x5  (20.0%)

cdc = results[1].get_causal_direction_counts(n_directions=8, min_causal_effect=0.01)
print_causal_directions(cdc, 100)

x0 <--- x3  (100.0%)
x1 <--- x0  (100.0%)
x1 <--- x2  (100.0%)
x2 <--- x3  (100.0%)
x4 <--- x0  (100.0%)
x4 <--- x2  (100.0%)
x5 <--- x0  (100.0%)
x1 <--- x3  (72.0%)


## 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 = results[0].get_directed_acyclic_graph_counts(n_dags=3, min_causal_effect=0.01)
print_dagc(dagc, 100)

DAG[0]: 61.0%
x0 <--- x3
x1 <--- x0
x1 <--- x2
x2 <--- x3
x4 <--- x0
x4 <--- x2
x5 <--- x0
DAG[1]: 13.0%
x0 <--- x3
x1 <--- x0
x1 <--- x2
x2 <--- x3
x4 <--- x0
x4 <--- x2
x4 <--- x5
x5 <--- x0
DAG[2]: 6.0%
x0 <--- x3
x1 <--- x0
x1 <--- x2
x2 <--- x3
x4 <--- x0
x5 <--- x0

dagc = results[1].get_directed_acyclic_graph_counts(n_dags=3, min_causal_effect=0.01)
print_dagc(dagc, 100)

DAG[0]: 59.0%
x0 <--- x3
x1 <--- x0
x1 <--- x2
x1 <--- x3
x2 <--- x3
x4 <--- x0
x4 <--- x2
x5 <--- x0
DAG[1]: 17.0%
x0 <--- x3
x1 <--- x0
x1 <--- x2
x2 <--- x3
x4 <--- x0
x4 <--- x2
x5 <--- x0
DAG[2]: 10.0%
x0 <--- x2
x0 <--- x3
x1 <--- x0
x1 <--- x2
x1 <--- x3
x2 <--- x3
x4 <--- x0
x4 <--- x2
x5 <--- x0


## Probability¶

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

prob = results[0].get_probabilities(min_causal_effect=0.01)
print(prob)

[[0.   0.   0.08 1.   0.   0.  ]
[1.   0.   1.   0.08 0.   0.05]
[0.   0.   0.   1.   0.   0.  ]
[0.   0.   0.   0.   0.   0.  ]
[1.   0.   0.94 0.   0.   0.2 ]
[1.   0.   0.   0.   0.01 0.  ]]


## Total Causal Effects¶

Using the get_total_causal_effects() method, we can get the list of total causal effect. The total causal effects we can get are dictionary type variable. We can display the list nicely by assigning it to pandas.DataFrame. Also, we have replaced the variable index with a label below.

causal_effects = results[0].get_total_causal_effects(min_causal_effect=0.01)
df = pd.DataFrame(causal_effects)

labels = [f'x{i}' for i in range(X1.shape[1])]
df['from'] = df['from'].apply(lambda x : labels[x])
df['to'] = df['to'].apply(lambda x : labels[x])
df

from to effect probability
0 x3 x0 3.005604 1.00
1 x0 x1 2.990264 1.00
2 x2 x1 2.091170 1.00
3 x3 x1 20.937520 1.00
4 x3 x2 5.969457 1.00
5 x0 x4 7.992477 1.00
6 x3 x4 18.058717 1.00
7 x0 x5 3.970275 1.00
8 x3 x5 12.028240 1.00
9 x5 x1 0.148078 0.29
10 x5 x4 0.104561 0.21
11 x2 x5 0.152502 0.15
12 x5 x2 0.078391 0.09
13 x2 x0 0.035852 0.08
14 x4 x1 -1.623188 0.03
15 x4 x5 0.027130 0.01

We can easily perform sorting operations with pandas.DataFrame.

df.sort_values('effect', ascending=False).head()

from to effect probability
3 x3 x1 20.937520 1.0
6 x3 x4 18.058717 1.0
8 x3 x5 12.028240 1.0
5 x0 x4 7.992477 1.0
4 x3 x2 5.969457 1.0

And with pandas.DataFrame, we can easily filter by keywords. The following code extracts the causal direction towards x1.

df[df['to']=='x1'].head()

from to effect probability
1 x0 x1 2.990264 1.00
2 x2 x1 2.091170 1.00
3 x3 x1 20.937520 1.00
9 x5 x1 0.148078 0.29
14 x4 x1 -1.623188 0.03

Because it holds the raw data of the causal effect (the original data for calculating the median), it is possible to draw a histogram of the values of the causal effect, as shown below.

import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
%matplotlib inline

from_index = 3
to_index = 0
plt.hist(results[0].total_effects_[:, to_index, from_index])


## 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 [3, 0, 1] shows the path from variable X3 through variable X0 to variable X1.

from_index = 3 # index of x3
to_index = 1 # index of x0

pd.DataFrame(results[0].get_paths(from_index, to_index))

path effect probability
0 [3, 0, 1] 8.561128 1.00
1 [3, 2, 1] 11.622379 1.00
2 [3, 1] 0.151715 0.08
3 [3, 2, 0, 1] 0.618533 0.08
4 [3, 0, 5, 1] 0.967472 0.05