import matplotlib.pyplot as plt
from datetime import datetime
SMALL_SIZE = 8
MEDIUM_SIZE = 26
BIGGER_SIZE = 30
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=BIGGER_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=MEDIUM_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
[docs]def plot_treatment_outcome(treatment, outcome, time_var):
fig, ax = plt.subplots()
tline = ax.plot(time_var, treatment, 'o', label="Treatment")
oline = ax.plot(time_var, outcome, 'r^', label="Outcome")
ax.legend(loc="upper left", bbox_to_anchor=(1.04, 1))
plt.xlabel("Time")
fig.set_size_inches(8, 6)
fig.savefig("obs_data" + datetime.now().strftime("%H-%M-%S") + ".png",
bbox_inches="tight")
[docs]def plot_causal_effect(estimate, treatment, outcome):
fig, ax = plt.subplots()
x_min = 0
x_max = max(treatment)
y_min = estimate.params["intercept"]
y_max = y_min + estimate.value * (x_max - x_min)
ax.scatter(treatment, outcome, c="gray", marker="o", label="Observed data")
ax.plot([x_min, x_max], [y_min, y_max], c="black", ls="solid", lw=4,
label="Causal variation")
ax.set_ylim(0, max(outcome))
ax.set_xlim(0, x_max)
bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.9)
ax.text(10.8, 1, r"DoWhy estimate $\rho$ (slope) = " + str(round(estimate.value, 2)),
ha="right", va="bottom", size=20, bbox=bbox_props)
ax.legend(loc="upper left")
plt.xlabel("Treatment")
plt.ylabel("Outcome")
fig.set_size_inches(8, 6)
fig.savefig("effect" + datetime.now().strftime("%H-%M-%S") + ".png",
bbox_inches='tight')