Recently, "DID; Difference-In-Difference" estimation method in the Treatment/Causal Effect has been regarded as one of the emerging and powerful methods for policy impact assessment, that estimates Average Treatment/Causal Effects by the difference of treatment and control groups for their outcome parameter's before and after difference of the policy treatment implementation. But practical application of conventional DID requires several assumptions to be fulfilled and if those assumptions are violated, a certain level of bias and unignorable error remains even if the case of experimental study with proper randomization measures or observational study with matching is applied. So DID application requires several assumptions to be confirmed and paid enough attention. This paper reviews necessary DID assumptions pointed out in the existing studies. Then this paper also focus on the "SUTVA: stable unit treatment value assumption" and develops a series of measures to detect and correct the bias associated with secondary and/or indirect impact of treatment on the control groups using time-series regression analysis for observational study, even in difficult cases such as wholesale market trade or oligopoly equilibrium exists and SUTVA violation by secondary and/or indirect impact of treatment on the control groups often exist or only a few samples are available. In order to confirm validity and effectiveness of the newly developed measures in practical application, this paper provides analysis for rumor-based damage on Fukushima agricultural products such as meat traded in the Tokyo Metropolitan Wholesale Market or rice in the wholesale market where SUTVA violation matters assuming the nuclear accident as a treatment. Further, this paper proposes "standard DID estimation procedures" to ensure necessary assumptions are fulfilled and possible biases are minimized based on these analysis. These outcomes are expected to contribute observational policy impact assessment in Aviation, Telecommunication, or Electricity/Gas wholesale market cases where the number of companies are limited and it is hard to get a large number of samples and control group free from the secondary and/or indirect impact of policy treatment or cases where experimental approach are hard to implement by some reason.