Can you show me how to solve it ?

1. Causality Imagine that we are interested in analyzing a government program. We consider individuals as treated if they participated in the program (and untreated if they did not). Following the notation of the Rubin causal model, imagine that we observe the following sample (which would be impossible observe in real life): Table: Imaginary dataset i Trt. y1 yo 1 0 17 8 2 0 7 5 3 0 10 4 4 1 5 1 5 1 O 6 1 4 la. Calculate and report the treatment effect for each individual (i.e., T;). 1b. Is the treatment effect heterogeneous or homogeneous? Briefly explain your answer. 1c. Calculate and interpret the average treatment effect for the sample. 1d. What does it mean if ; 0 for another individual? le. Estimate the average treatment effect by comparing the mean of the treatment group to the mean of the control group. Report your estimate. 1f. Should we expect our estimator in le to provide unbiased estimates? Explain. 1g. Why would it be impossible to actually observe all of the data in the table (in real life)? Specifically: Which parts of the dataset would we not observe in real life? Think about the fundametal problem of causal inference. 1h. Define and explain selection bias. 1i. Calculate (and report) the selection bias in this sample