  In this paper, we consider the problem of numerical investigation of the
counting statistics for a class of one-dimensional systems. Importance
sampling, the cornerstone technique usually implemented for such problems,
critically hinges on selecting an appropriate biased distribution. While
exponential tilt in the observable stands as the conventional choice for
various problems, its efficiency in the context of counting statistics may be
significantly hindered by the genuine discreteness of the observable. To
address this challenge, we propose an alternative strategy which we call
importance sampling with the local tilt. We demonstrate the efficiency of the
proposed approach through the analysis of three prototypical examples: a set of
independent Gaussian random variables, Dyson gas, and Symmetric Simple
Exclusion Process (SSEP) with a steplike initial condition.
