Blog: How causal 5

df = pd.read_csv("..\\dataset\\ecommerce_sample.csv")
df[time_axis] = pd.to_datetime(df[time_axis],format = '%d/%m/%Y')

#get kpi_axis
kpi_axis = 'kpi'
time_axis = 'time'
y_before =  df[df[time_axis] <= '2019-09-11'][kpi_axis]
y_after  =  df[df[time_axis]  > '2019-09-11'][kpi_axis]


df = binarize(df,df.columns,kpi_axis,time_axis,3)
df_before = df[df[time_axis] <= '2019-09-11']
df_after  = df[df[time_axis] > '2019-09-11']
features = copy(df.drop([time_axis,kpi_axis], axis=1).columns)

K = 3 #number of subgroups to detect
subgroups=[]
score=[]
for k in range(0,K):
    CATE = []
    y_before = df_before[kpi_axis]
    y_after= df_after[kpi_axis]
    
    #compute CATEs for all subgroups
    for d in features:
        g = df_before[d] == True
        m_before = np.mean(y_before[g])
        g = df_after[d] == True
        m_after = np.mean(y_after[g])
        CATE.append(m_after-m_before)
    
    #find subgroup with biggest CATE
    index = np.argsort(-abs(np.array(CATE)))
    subgroups.append(features[index[0]])
    score.append(abs( CATE [index[0]]))
    
    #remove found subgroups from dataset
    df_before = df_before[df_before[features[index[0]]] == False]
    df_after = df_after[df_after[features[index[0]]] == False] 
    features = features.drop(features[index[0]])
    
p = figure(x_range=subgroups,title = "Conditional Average Treatment Effect",plot_width=1200,)
p.vbar(x=subgroups, top=score,width = 0.8,color='black')
show(p)