CausalPy/test_est.py at main · CausalDataScience/CausalPy · GitHub

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import time
import numpy as np
import random
import pyperclip
import graph
import identify
import examples
import random_generator
import SCM
import adjustment
import frontdoor
import mSBD
import tian


if __name__ == "__main__":
	''' Predefined examples '''
	# [graph_dict, node_positions, X, Y] = examples.Tikka1()
	# [graph_dict, node_positions, X, Y] = examples.Tikka2()
	# [graph_dict, node_positions, X, Y] = examples.FD0()
	# [graph_dict, node_positions, X, Y] = examples.FD1()
	# [graph_dict, node_positions, X, Y] = examples.FD2() # answer: {A,B,C,D}, minimal = {D} or {A}
	# [graph_dict, node_positions, X, Y] = examples.FD3() # answer: {A,B,C,D}, minimal = {A}
	# [graph_dict, node_positions, X, Y] = examples.FD4() # answer: {A,B,C,D,E}, minimal = {BDE} or {ADE} | Z={B} or {A}, C={DE}
	# [graph_dict, node_positions, X, Y] = examples.FD5() # answer: {A,B,D}, minimal = {A,D} or Z={A}, C={D}
	# [graph_dict, node_positions, X, Y] = examples.UCA1()
	# [graph_dict, node_positions, X, Y] = examples.UCA2()
	# [graph_dict, node_positions, X, Y] = examples.BD_vdZ()
	# [graph_dict, node_positions, X, Y] = examples.BD_minimum()
	# [graph_dict, node_positions, X, Y] = examples.BD_minimum2()
	# [graph_dict, node_positions, X, Y] = examples.BD_minimum3()
	# [graph_dict, node_positions, X, Y] = examples.Tian1()
	# [graph_dict, node_positions, X, Y] = examples.Tian2()
	# [graph_dict, node_positions, X, Y] = examples.Tian3()
	# [graph_dict, node_positions, X, Y] = examples.Tian_mSBD()
	# [graph_dict, node_positions, X, Y] = examples.mSBD1()
	# [graph_dict, node_positions, X, Y] = examples.mSBD2()
	# [graph_dict, node_positions, X, Y] = examples.mSBD3()
	# [graph_dict, node_positions, X, Y] = examples.mSBD_minimum()
	# [graph_dict, node_positions, X, Y] = examples.Napkin_FD()
	# [graph_dict, node_positions, X, Y] = examples.Napkin()
	# [graph_dict, node_positions, X, Y] = examples.Double_Napkin()
	# [graph_dict, node_positions, X, Y] = examples.Chris_FD1()
	# [graph_dict, node_positions, X, Y] = examples.unID1()
	# [graph_dict, node_positions, X, Y] = examples.unID2()
	# [graph_dict, node_positions, X, Y] = examples.Rina1()
	# [graph_dict, node_positions, X, Y] = examples.Verma2()
	# [graph_dict, node_positions, X, Y] = examples.BNS1()
	# [graph_dict, node_positions, X, Y] = examples.BNS2()
	# [graph_dict, node_positions, X, Y] = examples.BNS3()

	# Generate the random graph
	seednum = int(time.time())
	# seednum = 1724430089
	np.random.seed(seednum)
	random.seed(seednum)
	[graph_dict, node_positions, X, Y] = random_generator.random_graph_generator(num_observables = 6, num_unobservables = 3, num_treatments = 2, num_outcomes = 1,
																			condition_ID = True,
																			condition_BD = False,
																			condition_mSBD = False,
																			condition_FD = False,
																			condition_Tian = False,
																			condition_gTian = False,
																			condition_product = True,
																			seednum = seednum)
	# graph_dict = {'U_V1_X3': ['V1', 'X3'], 'V1': ['X1'], 'U_V1_X2': ['X2', 'V1'], 'X2': ['X1', 'V1', 'V2', 'Y1'], 'U_V1_Y1': ['Y1', 'V1'], 'Y1': [], 'X3': ['X2', 'X1', 'V1'], 'U_X1_X3': ['X3', 'X1'], 'X1': ['V2'], 'V2': ['Y1']}
	# X = ['X1','X2']; Y = ['Y1']; node_positions = None
	G = graph.create_acyclic_graph(graph_dict=graph_dict, an_Y_graph_TF = False, Y = None, node_positions = node_positions)

	# Generate the random SCM
	# [scm, X, Y] = random_generator.Random_SCM_Generator(num_observables = 5, num_unobservables = 3, num_treatments = 2, num_outcomes = 1,
	# 																		condition_ID = True, condition_BD = False, condition_mSBD = False, condition_FD = False, condition_Tian = False, condition_gTian = True)
	# sample_data = scm.generate_samples(10000)[topo_V]
	# print(sample_data)
	# G = scm.graph


	# Visualize the graph
	# graph.visualize(G)

	# Identify the causal effect P(Y | do(X)) from G
	# G, X, Y = identify.preprocess_GXY_for_ID(G, X, Y)
	print( identify.causal_identification(G,X,Y, latex = False, copyTF=True) )


	# Draw the C-tree and AC-tree
	# identify.draw_C_tree(G,X,Y)
	# identify.draw_AC_tree(G,X,Y)

	adj_dict_components, adj_dict_operations = identify.return_AC_tree(G, X, Y)

	# Copy the graph for comparing with Fusion
	pyperclip.copy(graph.graph_dict_to_fusion_graph(graph_dict))