@@ -13,52 +13,50 @@ A Python package focussing on causal inference for quasi-experiments. The packag
1313To get the latest release you can use pip:
1414
1515``` bash
16- pip install CausalPy
16+ pip install CausalPy
1717```
1818
1919or conda:
2020
2121``` bash
22- conda install causalpy -c conda-forge
22+ conda install causalpy -c conda-forge
2323```
2424
2525Alternatively, if you want the very latest version of the package you can install from GitHub:
2626
2727``` bash
28- pip install git+https://github.com/pymc-labs/CausalPy.git
28+ pip install git+https://github.com/pymc-labs/CausalPy.git
2929```
3030
3131## Quickstart
3232
3333``` python
34-
35- import causalpy as cp
36- import matplotlib.pyplot as plt
37-
38-
39- # Import and process data
40- df = (
41- cp.load_data(" drinking" )
42- .rename(columns = {" agecell" : " age" })
43- .assign(treated = lambda df_ : df_.age > 21 )
44- )
45-
46- # Run the analysis
47- result = cp.RegressionDiscontinuity(
48- df,
49- formula = " all ~ 1 + age + treated" ,
50- running_variable_name = " age" ,
51- model = cp.pymc_models.LinearRegression(),
52- treatment_threshold = 21 ,
53- )
54-
55- # Visualize outputs
56- fig, ax = result.plot();
57-
58- # Get a results summary
59- result.summary()
60-
61- plt.show()
34+ import causalpy as cp
35+ import matplotlib.pyplot as plt
36+
37+
38+ # Import and process data
39+ df = (
40+ cp.load_data(" drinking" )
41+ .rename(columns = {" agecell" : " age" })
42+ .assign(treated = lambda df_ : df_.age > 21 )
43+ )
44+
45+ # Run the analysis
46+ result = cp.RegressionDiscontinuity(
47+ df,
48+ formula = " all ~ 1 + age + treated" ,
49+ running_variable_name = " age" ,
50+ model = cp.pymc_models.LinearRegression(),
51+ treatment_threshold = 21 ,
52+ )
53+
54+ # Visualize outputs
55+ fig, ax = result.plot()
56+ # Get a results summary
57+ result.summary()
58+
59+ plt.show()
6260```
6361
6462## Videos
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