Causal inference in conjoint analysis enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. This method addresses the limitations of traditional survey experiments, which often fail to isolate the specific components of a treatment that influence outcomes. Conjoint analysis, an experimental design not widely applied in political science, allows for the nonparametric identification and estimation of causal effects through a fully randomized design. By integrating conjoint analysis with the potential outcomes framework, researchers can decompose composite treatment effects and evaluate the relative influence of different components. The article introduces the average marginal component effect (AMCE) as a causal estimand that can be identified from conjoint data. It also proposes diagnostic checks for identification assumptions and demonstrates the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants. The study highlights the advantages of conjoint analysis, including its ability to test multiple causal hypotheses, enhance realism, and provide insights into practical problems such as policy design. The article presents empirical examples, including a study on U.S. citizens' preferences for presidential candidates and an experiment on attitudes toward immigrants, illustrating the application of conjoint analysis in political science. The proposed estimation strategies are nonparametric and can be implemented using standard statistical software, offering a more robust approach to causal inference compared to traditional methods.Causal inference in conjoint analysis enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. This method addresses the limitations of traditional survey experiments, which often fail to isolate the specific components of a treatment that influence outcomes. Conjoint analysis, an experimental design not widely applied in political science, allows for the nonparametric identification and estimation of causal effects through a fully randomized design. By integrating conjoint analysis with the potential outcomes framework, researchers can decompose composite treatment effects and evaluate the relative influence of different components. The article introduces the average marginal component effect (AMCE) as a causal estimand that can be identified from conjoint data. It also proposes diagnostic checks for identification assumptions and demonstrates the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants. The study highlights the advantages of conjoint analysis, including its ability to test multiple causal hypotheses, enhance realism, and provide insights into practical problems such as policy design. The article presents empirical examples, including a study on U.S. citizens' preferences for presidential candidates and an experiment on attitudes toward immigrants, illustrating the application of conjoint analysis in political science. The proposed estimation strategies are nonparametric and can be implemented using standard statistical software, offering a more robust approach to causal inference compared to traditional methods.