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	0.3041	0.6959
	1	0.7050	0.2950
1	0	0.4430	0.5570
	1	0.8131	0.1869

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	0.2616	0.7384
		1	0.1200	0.8800
	1	0	0.2828	0.7172
		1	0.0576	0.9424
1	0	0	0.4904	0.5096
		1	0.2703	0.7297
	1	0	0.5171	0.4829
		1	0.1424	0.8576

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	0.2861	0.7139
		1	0.0401	0.9599
	1	0	0.5212	0.4788
		1	0.1018	0.8982
1	0	0	0.2143	0.7857
		1	0.1668	0.8332
	1	0	0.4255	0.5745
		1	0.3522	0.6478

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	0.0485	0.3572
		1	0.0822	0.5121
	1	0	0.0845	0.2819
		1	0.1304	0.5032
1	0	0	0.1076	0.2917
		1	0.1824	0.4183
	1	0	0.1677	0.2059
		1	0.2588	0.3676