Appraise the statement: “For the model  = 0 + 1 + , 1 reflects the causal effect of  on .” Ask
Hi and welcome to the forum. Homework is not so well received here in the forum. But still a fair question in my view. 

You have two aspects here. In principle you are right that $\beta_1$ is the slope of $x_1$ (you can say the marginal effect of $x_1$ on $y$) and $\beta_0$ is the intercept. This is simply a linear function of form $f(x)=\beta_0 + \beta_1 x$. 

However, to claim "causality", a few more things are required. First, you need to make the assumption that there is a causal relation between $x$ and $y$ and $x$ must be exogenous. 

Another important aspect is, that if there are additional variables with a causal influence on $y$, say $x_2$, you cannot really claim that $\beta_1$ is the causal effect on $...$, because you omitted $x_2$, so that your model suffers from the omitted variable bias. To claim for causality you need to make sure that your model reflects the data generating process in a proper way.