How to decompose the determinants of an outcome I was looking at this graph about the gender pay gap.
I have a similar problem.
I have an binary outcome $Y$, a binary covariate $D$ for gender (with 1 meaning female), and a series of covariates $X_1$, $X_2$, etc... (continuous and binary).
I modeled a logistic regression of the outcome on gender and the covariates.
If I don't put the covariates in, the coefficient of gender is significant.
But if I control for the covariates, the coefficient of gender is not significant anymore.
So I guess that the outcome is determined by the $X$ covariates and not directly by gender, and that females don't get the outcome due to the covariates distributed differently across the two genders (if you split the outcome by gender, you see that the mean outcome for females is much less than the one for males).
How can I determine which covariates determine females don't getting the outcome, similar to the plot I posted above?

The graph that you link to is likely the result of a Blinder–Oaxaca decomposition. There are packages in R and Stata (and I'm sure many other statistical environments) to produce such a decomposition.
Note that, as in every regression-based analysis, the outcome does not tell you about whether the result of the decomposition can be interpreted as causal. I would therefore avoid language such as "covariates determine females don't getting the outcome".
