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The fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) has been used to solve various multi-criteria group decision-making problems where triangular fuzzy numbers are utilized in defining decision makers' linguistic judgements. Most of the fuzzy DEMATEL modifications are built from linguistic variables based on fuzzy sets. Recent literature suggests that Pythagorean fuzzy sets (PFS) can offer a better alternative particularly when fuzzy sets have some extent of limitations in handling vagueness and uncertainty. This paper proposes a modification fuzzy DEMATEL characterized by PFS for linguistic variables. Differently from the typical fuzzy DEMATEL which directly utilizes triangular fuzzy numbers with a single membership, this modification introduces membership and non-membership of PFS to enhance judgements in the group decision-making environment. The proposed method has a number of attractive features. It includes linguistic variables, expert's weights, and score function, in which all of these features are expressed by PFS. The proposed modification is applied to a case of solid waste management (SWM) where ten criteria are considered in assessment. Six experts in SWM were invited to provide linguistic judgments with respect to the criteria, and the eleven-step computational procedure of the proposed method was implemented without losing the general structure of the DEMATEL method. The results unveiled that four criteria are identified as 'cause group' and six criteria are identified as 'effect group' in SWM. The grouping of criteria would help policy makers in identifying the criteria that could enhance the efficiency of SWM. Keywords Pythagorean fuzzy set • DEMATEL • Causal diagram • Decision making • Solid waste management Develop seven-scale linguistic variable Construct direct-relation matrix with Pythagorean Fuzzy number Phase 2 PF-DEMATEL Construct an aggregated matrix Construct Weighted the Pythagorean Fuzzy matrix with different weigth of expert. Construct total average crisp matrix Construct normalized the average crisp matrix Construct total-relation matrix Calculate sum of row and column base on total-relation matrix Construct causal diagram Phase 3 Causal Diagram Network relationship map Phase 1 Define Linguistic variable

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