I'm building a regression model to predict the values of a feature $Y$ given a set of other features $X\_{1}, X\_{2}, X\_{3}..X\_{n}$.

Onde of these other features, let's say $X\_1$, is known to be inversely proportional to $Y$ based on domain's knowledge. The problem is my model is interpreting his coefficient as positive, letting it directly proportional to $Y$. I've tried plenty of different models to verify if I could get better interpretation, such as OLS, Linear Regression, and Logistic Regression, but every model I tried failed to interpret the $X\_1$ coefficient.

What can I do to get a regression that better reflects the real-world behavior of this coefficient?