Researchers have become increasingly interested in programs' main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. A challenge for estimating main and interaction effects is to eliminate selection bias across A-by-B groups. I introduce Rubin's causal model to approximate factorial experimental designs for studies with partial randomization and nonrandomization. I apply a Monte Carlo simulation to evaluate several propensity score applications. The findings suggest the following two applications for reducing bias and mean square error of parameter estimates when analyzing the relationship of two variables and an outcome: (a) inverse of propensity score weighting based on one multinomial propensity score model and (b) subclassification based on two binary propensity score models. As a demonstration, I examine whether the effects of the Head Start program, compared to other center-based care, for improving children's reading achievement vary by child care quality.