# Different ways to load an input graph¶

We recommend using the GML graph format to load a graph. You can also use the DOT format, which requires additional dependencies (either pydot or pygraphviz).

DoWhy supports both loading a graph as a string, or as a file (with the extensions ‘gml’ or ‘dot’).

Below is an example showing the different ways of loading the same graph.

```
[1]:
```

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

```
[2]:
```

```
import numpy as np
import pandas as pd
import dowhy
from dowhy import CausalModel
from IPython.display import Image, display
```

## I. Generating dummy data¶

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

```
[3]:
```

```
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
```

```
[3]:
```

Z | X | Y | |
---|---|---|---|

0 | 5 | 0 | 0 |

1 | 2 | 1 | 10 |

2 | 8 | 2 | 20 |

3 | 9 | 3 | 30 |

4 | 3 | 4 | 40 |

5 | 6 | 5 | 50 |

6 | 1 | 6 | 60 |

7 | 4 | 7 | 70 |

8 | 0 | 8 | 80 |

9 | 7 | 9 | 90 |

## II. Loading GML or DOT graphs¶

### GML format¶

```
[4]:
```

```
# 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']
```

```
[5]:
```

```
# 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']
```

### DOT format¶

```
[6]:
```

```
# 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']
```

```
[7]:
```

```
# 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']
```