The Propensity Score Matching-Differenced-Differences Model (PSM-DID) is one of the most popular methods for policy evaluation and causal inference. However, it faces the challenge of unbalanced control variables between the treatment group samples and the control group samples. The traditional balance test based on the mean difference t test is prone to one-sided and imprecise conclusions, which makes subsequent causal inferences biased and incredible. In order to overcome the above problems, this paper proposes the following improvements on the traditional balance test: First, it recommends a few more comprehensive and multi-dimensional balance measures, which facilitates (after applying propensity score matching) the balance comparison between the treated group and the control group in a more rigorous method. Second, a new estimation method for unbalanced samples is proposed: Propensity Score Matching-Inverse Probability Weighted-Differences (PSM-IPW-DID), which combines the merits of propensity score matching (PSM) to overcome sample self-selection endogeneity and robustness to unbalanced samples and the advantage of Inverse Probability Weighting (IPW) to take advantage of the full-sample information, to obtain a more robust difference-in-differences estimation method without further trimming samples. Furthermore, numerical simulation and real data applications show that the proposed new method can evaluate the effects of various macro and micro policies comprehensively and objectively, and yield credible causal inferences.