Table 1.

Overview of three classes of causal effect estimator

G-computation estimator
Needed for implementation	Estimator Qn of Q0
Needed for consistency	Qn is a consistent estimator of Q0
Response to sparsity	Extrapolates based on Qn
	Sparsity can amplify bias due to model misspecification
IPTW estimator
Needed for implementation	Estimator gn of g0
Needed for consistency	gn is a consistent estimator of g0
	g0 satisfies positivity
Response to sparsity	Does not extrapolate based on Qn
	Sensitive to positivity violations and near violations
DR estimators
Needed for implementation	Estimator gn of g0 and Qn of Q0
Needed for consistency	gn is consistent or Qn is consistent
	gn converges to a distribution that satisfies positivity
Response to sparsity	Can extrapolate based on Qn
	Without positivity, relies on consistency of Qn