  In this paper, we propose a framework for achieving long-term fair sequential
decision making. By conducting both the hard and soft interventions, we propose
to take path-specific effects on the time-lagged causal graph as a quantitative
tool for measuring long-term fairness. The problem of fair sequential decision
making is then formulated as a constrained optimization problem with the
utility as the objective and the long-term and short-term fairness as
constraints. We show that such an optimization problem can be converted to a
performative risk optimization. Finally, repeated risk minimization (RRM) is
used for model training, and the convergence of RRM is theoretically analyzed.
The empirical evaluation shows the effectiveness of the proposed algorithm on
synthetic and semi-synthetic temporal datasets.
