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We included these scores as covariates in the outcome models.
Exploratory analyses suggested that the propensity score should be modeled nonlinearly into the outcome models.
Residual differences in baseline characteristics after matching were included in the outcome models.
The estimated weights were then applied to the outcome models described previously.
Any variable staying in any of the outcome models, was included in all the models, for model consistency.
A missing value indicator was incorporated in the outcome models for covariates if the number of participants with missing information was ≥100.
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Results of the outcome model for the wet lowland condition are also portrayed in Table 8.
Similarly, the weights correct the RA estimator if the treatment model is correct but the outcome model is wrongly specified.
If the outcome model is correctly specified, the weights do not affect the consistency of the estimator (Wooldridge 2007).
Thus, by weighting both groups with their inverse probability of treatment in the outcome model, the composition of the sample which has received treatment t is re-balanced.
In the case of the outcome model, the dependent variable is whether a farmer has adapted or not to climate change.
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