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EDIT: In order to test the overall moderating effect of the moderate X I've conducted a Wald Test like so:

library(estimatr)
library(lmtest)
m1 <- lm_robust(outcome ~ treatment / X - 1, data = data)
m2 <- lm_robust(outcome ~ treatment - 1, data = data)
waldtest(m1, m2)

Wald test

Model 1: outcome ~ treatment/X - 
    1
Model 2: outcome ~ treatment - 1
  Res.Df Df Chisq Pr(>Chisq)    
1    640                        
2    644 -4  18.9    0.00084 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

How do I interpret this? I assume this is evidence that of an overall moderating effect of X, but how do I interpret 18.9?

Final Edit:

                                 Estimate Std. Error  t value  Pr(>|t|)  CI Lower CI Upper  DF
treatmentA                       -1.33113    4.96200 -0.26826 0.7889642 -11.15724  8.49498 118
treatmentB                       -7.23550    4.85805 -1.48938 0.1390545 -16.85577  2.38477 118
treatmentC                       23.59638    7.83199  3.01282 0.0031679   8.08690 39.10586 118
treatmentD                        5.93834    2.88122  2.06105 0.0414956   0.23274 11.64395 118
treatmentA :moderator             0.65245    1.27021  0.51366 0.6084530  -1.86291  3.16782 118
treatmentB :moderator             5.15284    1.64776  3.12717 0.0022231   1.88982  8.41586 118
treatmentC :moderator            -2.65812    1.08513 -2.44959 0.0157709  -4.80698 -0.50927 118
treatmentD :moderator            -0.69822    0.67031 -1.04163 0.2997141  -2.02562  0.62919 118

I'm a little confused on how to interpret the interaction effects. It looks like, for treatment B, every additional increase in the moderator value increases the outcome by 5. However, the main effect of treatment B is -7.2. Meanwhile, the effect of the moderator on treatment C is negative (-2.6), but the main effect is large and positive (23.59). As a result, the interaction terms on treatmentC may be misleading because it's unlikely that the combined effect of the moderator + treatment will ever be overall negative. Is there a way to characterize this nuance?

Final Edit:

                                 Estimate Std. Error  t value  Pr(>|t|)  CI Lower CI Upper  DF
treatmentA                       -1.33113    4.96200 -0.26826 0.7889642 -11.15724  8.49498 118
treatmentB                       -7.23550    4.85805 -1.48938 0.1390545 -16.85577  2.38477 118
treatmentC                       23.59638    7.83199  3.01282 0.0031679   8.08690 39.10586 118
treatmentD                        5.93834    2.88122  2.06105 0.0414956   0.23274 11.64395 118
treatmentA :moderator             0.65245    1.27021  0.51366 0.6084530  -1.86291  3.16782 118
treatmentB :moderator             5.15284    1.64776  3.12717 0.0022231   1.88982  8.41586 118
treatmentC :moderator            -2.65812    1.08513 -2.44959 0.0157709  -4.80698 -0.50927 118
treatmentD :moderator            -0.69822    0.67031 -1.04163 0.2997141  -2.02562  0.62919 118

I'm a little confused on how to interpret the interaction effects. It looks like, for treatment B, every additional increase in the moderator value increases the outcome by 5. However, the main effect of treatment B is -7.2. Meanwhile, the effect of the moderator on treatment C is negative (-2.6), but the main effect is large and positive (23.59). As a result, the interaction terms on treatmentC may be misleading because it's unlikely that the combined effect of the moderator + treatment will ever be overall negative. Is there a way to characterize this nuance?