  Recent methods to improve generalizations from nonrandom samples typically
invoke assumptions such as the strong ignorability of sample selection that are
often controversial in practice to derive point estimates. Rather than focus on
the point estimate based inferences, this article considers inferences on
partially identified estimates from fewer and weaker assumptions. We extend
partial identification methods to causal generalization with nonrandom samples
by using a cluster randomized trial in education. Bounds on the population
average treatment effect are derived under four cases, two under no assumptions
on the data, and two that assume bounded sample variation and monotonicity of
response. This approach is amenable to incorporating population data frames to
tighten bounds on the population average treatment effect. Under the
assumptions of bounded sample variation and monotonicity, the interval
estimates of the average treatment effect provide sufficiently informative
bounds to rule out large treatment effects, which are consistent with the point
estimates from the experimental study. This illustrates that partial
identification methods can provide an alternative perspective to causal
generalization in the absence of strong ignorability of sample selection.
