# CAM-UV

## Model

This method CAM-UV (Causal Additive Models with Unobserved Variables) [2] assumes an extension of the basic CAM model [1] to include unobserved variables. This method makes the following assumptions:

The effects of direct causes on a variable form generalized additive models (GAMs).

The causal structures form directed acyclic graphs (DAGs).

CAM-UV allows the existence of unobserved variables. It outputs a causal graph where a undirected edge indicates the pair of variables that have an unobserved causal path (UCP) or an unobserved backdoor path (UBP), and a directed edge indicates the causal direction of a pair of variables that do not have an UCP or UBP.

**Definition of UCPs ans UBPs:** As shown in the below figure, a causal path from \(x_j\) to \(x_i\) is called an UCP if it ends with the directed edge connecting \(x_i\) and its unobserved direct cause. A backdoor path between \(x_i\) and \(x_j\) is called an UBP if it starts with the edge connecting \(x_i\) and its unobserved direct cause, and ends with the edge connecting \(x_j\) and its unobserved direct cause.

References

## Import and settings

In this example, we need to import `numpy`

in addition to `lingam`

.

```
import numpy as np
import random
import lingam
```

## Test data

First, we generate a causal structure with 2 unobserved variables (y6 and y7) and 6 observed variables (x0–x5) as shown in the below figure.

```
def get_noise(n):
noise = ((np.random.rand(1, n)-0.5)*5).reshape(n)
mean = get_random_constant(0.0,2.0)
noise += mean
return noise
def causal_func(cause):
a = get_random_constant(-5.0,5.0)
b = get_random_constant(-1.0,1.0)
c = int(random.uniform(2,3))
return ((cause+a)**(c))+b
def get_random_constant(s,b):
constant = random.uniform(-1.0, 1.0)
if constant>0:
constant = random.uniform(s, b)
else:
constant = random.uniform(-b, -s)
return constant
def create_data(n):
causal_pairs = [[0,1],[0,3],[2,4]]
intermediate_pairs = [[2,5]]
confounder_pairs = [[3,4]]
n_variables = 6
data = np.zeros((n, n_variables)) # observed data
confounders = np.zeros((n, len(confounder_pairs))) # data of unobserced common causes
# Adding external effects
for i in range(n_variables):
data[:,i] = get_noise(n)
for i in range(len(confounder_pairs)):
confounders[:,i] = get_noise(n)
confounders[:,i] = confounders[:,i] / np.std(confounders[:,i])
# Adding the effects of unobserved common causes
for i, cpair in enumerate(confounder_pairs):
cpair = list(cpair)
cpair.sort()
data[:,cpair[0]] += causal_func(confounders[:,i])
data[:,cpair[1]] += causal_func(confounders[:,i])
for i1 in range(n_variables)[0:n_variables]:
data[:,i1] = data[:,i1] / np.std(data[:,i1])
for i2 in range(n_variables)[i1+1:n_variables+1]:
# Adding direct effects between observed variables
if [i1, i2] in causal_pairs:
data[:,i2] += causal_func(data[:,i1])
# Adding undirected effects between observed variables mediated through unobserved variables
if [i1, i2] in intermediate_pairs:
interm = causal_func(data[:,i1])+get_noise(n)
interm = interm / np.std(interm)
data[:,i2] += causal_func(interm)
return data
sample_size = 2000
X = create_data(sample_size)
```

## Causal Discovery

To run causal discovery, we create a `CAMUV`

object and call the `fit`

method.

```
model = lingam.CAMUV()
model.fit(X)
```

Using the `adjacency_matrix_`

properties, we can see the adjacency matrix as a result of the causal discovery. When the value of a variable pair is np.nan, the variables have a UCP or UBP.

```
model.adjacency_matrix_
```

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