The order in which people observe data has an effect on their subsequent judgments and inferences. While Bayesian models of cognition have had success in predicting human inferences, most of these models do not produce order effects, being unaffected by the order in which data are observed. Recent work has explored approximations to Bayesian inference that make the underlying computations tractable, and also produce order effects in a way that seems consistent with human behavior. One of the most popular approximations of this kind is a sequential Monte Carlo method known as a particle filter. However, there has not been a systematic investigation of how the parameters of a particle filter influence its predictions. In this paper, we use a causal learning task as the basis for an investigation of these issues and we demonstrate that different order effects can result from varying the parameters of a particle filter.