This paper develops general numerical and causal interpretations of the two-way fixed effects (TWFE) estimator in any multiperiod panel. At the sample level, the TWFE coefficient is a weighted average of first difference regression coefficients using all possible between-period gaps. This decomposition improves transparency by revealing the sources of variation that the TWFE coefficient captures. At the population level, a causal interpretation of the TWFE coefficient requires a common trends assumption for any between-period gap, conditional on changes, not levels, of time-varying covariates. I propose a simple modification to the TWFE approach that naturally relaxes these requirements.