我如何运行pycausalimpact来确定和度量因果关系？

Question

我最近遇到了皮克

https://pypi.org/project/pycausalimpact/

import numpy as np
import pandas as pd
from statsmodels.tsa.arima_process import ArmaProcess
from causalimpact import CausalImpact


np.random.seed(12345)
ar = np.r_[1, 0.9]
ma = np.array([1])
arma_process = ArmaProcess(ar, ma)
X = 100 + arma_process.generate_sample(nsample=100)
y = 1.2 * X + np.random.normal(size=100)
y[70:] += 5

data = pd.DataFrame({'y': y, 'X': X}, columns=['y', 'X'])
pre_period = [0, 69]
post_period = [70, 99]

ci = CausalImpact(data, pre_period, post_period)
print(ci.summary())
print(ci.summary(output='report'))
ci.plot()

在给定的示例中，该通用代码运行良好。现在，我尝试在pycausalimpact示例中运行我自己的数据，如下所示

import sys
import os
import numpy as np
import pandas as pd
from IPython.core.pylabtools import figsize
import statsmodels as sm
from statsmodels.tsa.statespace.structural import UnobservedComponents
from statsmodels.tsa.arima_process import ArmaProcess
from matplotlib import pyplot as plt
from causalimpact import CausalImpact
import warnings

y = fus['days']
X = fus[['market_cat',
               'mmepool_cat',
               'submarket_cat',
               'local_market_cat',
               'project_type_cat',
               'site_status_cat',
               'city_cat',
               'state_cat']]

我正在努力解决的问题是：

ci = CausalImpact(data, pre_period, post_period)
print(ci.summary())
print(ci.summary(output='report'))
ci.plot()

我需要如何准备“数据”、“前期”和“后期”，才能在我的特定数据集中工作？这是我的一些实际数据

y=

X=

基本上，我想看看自变量和因变量（天数）之间是否存在因果关系。或者……是否有更好的/替代方法来确定和衡量因果关系？谢谢