However, because they are not identifiable without any assumptions, various assumptions have been utilized to evaluate the joint probabilities of potential outcomes, e.g., the assumption of monotonicity (Pearl, 2009; Tian and Pearl, 2000), the independence between potential outcomes (Robins and Richardson, 2011), the condition of gain equality (Li and Pearl, 2019), and the specific functional relationshipsbetween cause and effect (Pearl, 2009). Unlike existing identification conditions, in order to evaluate the joint probabilities of potential outcomeswithoutsuch assumptions,this paper proposestwo types of novel identification conditions using covariate information. In addition, when the joint probabilities of potential outcomes are identifiable through the proposed conditions, the estimation problem of the joint probabilities of potential outcomes reduces to that of singular models and thus they can not be evaluated by standard statistical estimation methods. To solve the problem,this paper proposes a new statisticalestimationmethod based on the augmented Lagrangianmethod and shows the asymptoticnormality of the proposed estimators. Given space constraints, the proofs, the details on the statistical estimationmethod, some numerical experiments, and the case study are provided in the supplementary material.

For (see 6 fordetails), densityestimation6, 8, 53].

The ability to perform causal reasoning is widely considered a core feature of intelligence.

Identifying the effects of new interventions from data is a significant challenge found across a wide range of the empirical sciences. A well-known strategy for identifying such effects is Pearl's front-door (FD) criterion [