For causal sequence models—those in which time progresses naturally forward—estimating values in the forward direction (predicting the future from past observations) is typically much easier and more practical than estimating in the reverse direction (reconstructing the past from future observations). This asymmetry arises because causal models are designed to capture how past events generate future outcomes, making the forward conditional distribution the natural object of estimation. Attempting to invert this process is inherently more challenging because the mapping from effects back to causes is often one-to-many and ill-conditioned.