  Suppose that a sequence of treatments are assigned to influence an outcome of
interest that occurs after the last treatment. Between treatments there exist
time-dependent covariates that may be posttreatment variables of the earlier
treatments and confounders of the subsequent treatments. In this article, we
develop a parametric approach to inference of the causal effect of the
treatment sequence on the outcome called the sequential causal effect. We
construct a point parametrization for the conditional distribution of an
outcome given all treatments and time-dependent covariates, in which the point
parameters of interest are the point effects of treatments considered as
single-point treatments. We (1) identify net effects of treatments by point
effects of treatments, (2) express patterns of net effects of treatments by
constraints on point effects of treatments, and (3) show that all sequential
causal effects are determined by net effects of treatments. Accordingly we (1)
estimate net effects of treatments through point effects of treatments by
maximum likelihood, (2) improve the estimation by constraints on point effects
of treatments and assignment conditions of treatments, and (3) use the
estimates of net effects of treatments to obtain those of sequential causal
effects. As a result, we obtain unbiased consistent maximum-likelihood
estimates of sequential causal effects even for long treatment sequences. For
illustration of our method, we study the causal effects of various sequences of
recreational drugs on the CD4 count among HIV patients.
