This pyAgrum's notebook is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

import pyAgrum as gum
import pyAgrum.lib.notebook as gnb

bn=gum.fastBN("A->B->C->D;A->E->D;F->B;C->H")
ie=gum.LazyPropagation(bn)
bn              

Evidence Impact allows the user to analyze the effect of any variables on any other variables

ie.evidenceImpact("B",["A","H"])

		B
A	H	0	1
0	0	0.5017	0.4983
	1	0.4142	0.5858
1	0	0.7911	0.2089
	1	0.7267	0.2733

Evidence impact is able to find the minimum set of variables which effectively conditions the analyzed variable

ie.evidenceImpact("E",["A","F","B","D"]) # {A,D,B} d-separates E and F

			E
A	B	D	0	1
0	0	0	0.4589	0.5411
		1	0.2693	0.7307
	1	0	0.4751	0.5249
		1	0.2418	0.7582
1	0	0	0.1460	0.8540
		1	0.0691	0.9309
	1	0	0.1543	0.8457
		1	0.0604	0.9396

ie.evidenceImpact("E",["A","B","C","D","F"]) # {A,C,D} d-separates E and {B,F}

			E
C	A	D	0	1
0	0	0	0.4964	0.5036
		1	0.1915	0.8085
	1	0	0.1658	0.8342
		1	0.0456	0.9544
1	0	0	0.4060	0.5940
		1	0.3259	0.6741
	1	0	0.1211	0.8789
		1	0.0888	0.9112

ie.evidenceJointImpact(["A","F"],["B","C","D","E","H"]) # {B,E} d-separates [A,F] and [C,D,H]

			A
E	B	F	0	1
0	0	0	0.4496	0.2457
		1	0.2572	0.0475
	1	0	0.8583	0.0640
		1	0.0423	0.0354
1	0	0	0.2080	0.5640
		1	0.1190	0.1090
	1	0	0.6159	0.2276
		1	0.0303	0.1261