Skip to main content
. Author manuscript; available in PMC: 2010 Oct 1.
Published in final edited form as: J Stat Plan Inference. 2009 Oct 1;139(10):3473–3487. doi: 10.1016/j.jspi.2009.03.024

TABLE 1.

Analytic Bounds on Risk Difference Causal Effects in the Pretreatment-covariate DAG [Z →X →Y, Z →Y] under Various Assumptions

Assumption1 Effect Measure2 Lower Bound Upper Bound
None RDC −Pr(Y≠X) Pr(Y=X)
RDC|Z=z −Pr(Y≠X|Z=z) Pr(Y=X|Z=z)
RDC|SET[Z=z] −Pr(Y≠X)−Pr(Y=X,Z≠z) Pr(Y=X)+Pr(Y≠X, Z≠z)

{1} RDC, RDC|SET[Z=z] −Pr(Y≠X) Pr(Y=X)
RDC|Z=z −Pr(Y≠X |Z=z) Pr(Y=X|Z=z)

{2} RDC 0 Pr(Y=X)
RDC|Z=z 0 Pr(Y=X|Z=z)
RDC|SET[Z=z] 0 Pr(Y=X)+Pr(Y≠X, Z≠z)

{3} RDC 0 Pr(Y=X)
RDC|Z=z 0 Pr(Y=X|Z=z)
RDC|SET[Z=z] 0 Pr(Y=X)+Pr(Y≠z, X=z, Z≠z)

{4} All 0 min{Pr(Y=X|Z=0), Pr(Y=X|Z=1)}

{5} RDC −Pr(Y≠X) Pr(Y=X)
RDC|Z=z, RDC|SET[Z=z] −Pr(Y≠X|Z=z) Pr(Y=X|Z=z)

{1,5} All max{a1,…, a8}3 min{b1,…, b8}3

{2,5} RDC 0 Pr(Y=X)
RDC|Z=z, RDC|SET[Z=z] 0 Pr(Y=X|Z=z)

{3,5} RDC 0
min{Pr(Y=X),1p(1,0|0)p(0,1|1),Pr(Y=X)+p(1,0,1)p(1,0,0),Pr(Y=X|Z=1)+p(1,0,1)p(1,0,0),}
RDC|Z=z, RDC|SET[Z=z] 0
min{Pr(Y=X|Z=z),1p(1,0|0)p(0,1|1)}

{4,5} All 0
min{Pr(Y=X|Z=0),Pr(Y=X|Z=1),Pr(Y=X|Z=0)+p(1,0|1)p(1,0|0),Pr(Y=X|Z=1)+p(1,0|0)p(1,0|1)}

{1,2}, {1,3}, {1,4} RDC, RDC|SET[Z=z] 0 Pr(Y=X)
RDC|Z=z 0 Pr(Y=X|Z=z)

{1,2,5}, {1,3,5}, {1,4,5} All Pr(Y=1|Z=1) − Pr(Y=1|Z=0) Pr(Y=1|Z=1) − Pr(Y=1|Z=0) + p(1,1| 0) + p(0,0| 1)
1
Assumptions are defined in Section 2.3
  • {1} No direct Z →Y effect
  • {2} Partial monotonicity
  • {3} Full monotonicity
  • {4} Full monotonicity and no interaction between Z and X
  • {5} Exogenous (e.g. randomized)
2
Effect Measures are defined in Section 2.2
  • RDC = Pr(Y=1| SET[X=1]) − Pr(Y=1| SET[X=0])
  • RDC|Z=z = Pr(Y=1| SET[X=1], Z=z) − Pr(Y=1| SET[X=0], Z=z)
  • RDC|SET[Z=z] = Pr(Y=1| SET[X=1], SET[Z=z]) − Pr(Y=1| SET[X=0], SET[Z=z])
4
p notation:
  • p(y,x,z) = Pr(Y=y, X=x, Z=z)
  • p(y,x | z)= Pr(Y=y, X=x | Z=z)