Different ways to load an input graph

我们建议使用GML图形格式加载因果图。您还可以使用DOT格式，该格式需要其他依赖项（pydot或pygraphviz）。DoWhy支持通过字符串或文件（扩展名为“ gml”或“ dot”）载入因果图。下面的示例显示了加载同一因果图的不同方式。

import os, sys
import random
sys.path.append(os.path.abspath("../../../"))

import numpy as np
import pandas as pd

import dowhy
from dowhy import CausalModel
from IPython.display import Image, display

We generate some dummy data for three variables: X, Y and Z.

z=[i for i in range(10)]
random.shuffle(z)
df = pd.DataFrame(data = {'Z': z, 'X': range(0,10), 'Y': range(0,100,10)})
df

	Z	X	Y
0	5	0	0
1	1	1	10
2	7	2	20
3	6	3	30
4	4	4	40
5	3	5	50
6	2	6	60
7	9	7	70
8	0	8	80
9	8	9	90

Loading GML graphs

首先看看如何载入 GML 格式的图。

# With GML string
model=CausalModel(
        data = df,
        treatment='X',
        outcome='Y',
        graph="""graph[directed 1 node[id "Z" label "Z"]
                    node[id "X" label "X"]
                    node[id "Y" label "Y"]
                    edge[source "Z" target "X"]
                    edge[source "Z" target "Y"]
                    edge[source "X" target "Y"]]"""

        )

model.view_model()
display(Image(filename="causal_model.png"))

INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named "Unobserved Confounders" to reflect this.
INFO:dowhy.causal_model:Model to find the causal effect of treatment ['X'] on outcome ['Y']

!cat ../example_graphs/simple_graph_example.gml

graph[
    directed 1
    node[ id "Z" label "Z"]
    node[ id "X" label "X"]
    node[ id "Y" label "Y"]
    edge[source "Z" target "X"]
    edge[source "Z" target "Y"]
    edge[source "X" target "Y"]
]

# With GML file
model=CausalModel(
        data = df,
        treatment='X',
        outcome='Y',
        graph="../example_graphs/simple_graph_example.gml"
        )
model.view_model()


display(Image(filename="causal_model.png"))

INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named "Unobserved Confounders" to reflect this.
INFO:dowhy.causal_model:Model to find the causal effect of treatment ['X'] on outcome ['Y']

Loading DOT graphs

DOT 格式的图也可以用于指定因果模型的图结构。

# With DOT string
model=CausalModel(
        data = df,
        treatment='X',
        outcome='Y',
        graph="digraph {Z -> X;Z -> Y;X -> Y;}"
        )
model.view_model()

from IPython.display import Image, display
display(Image(filename="causal_model.png"))

INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named "Unobserved Confounders" to reflect this.
INFO:dowhy.causal_model:Model to find the causal effect of treatment ['X'] on outcome ['Y']

cat ../example_graphs/simple_graph_example.dot

digraph G {Z -> X;Z -> Y;X -> Y;}

# With DOT file
model=CausalModel(
        data = df,
        treatment='X',
        outcome='Y',
        graph="../example_graphs/simple_graph_example.dot"
        )
model.view_model()


display(Image(filename="causal_model.png"))

INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named "Unobserved Confounders" to reflect this.
INFO:dowhy.causal_model:Model to find the causal effect of treatment ['X'] on outcome ['Y']