Agree or Disagree?

Discussion: discuss what is required to make causal statements in the context of regression analysis... In other words,what is required to be able to say that a particular independent variable, or treatment,causes the dependent variable?In your answer, discuss the role played by obtaining a random sample and random assignment of the "treatment" (in many cases, the treatment is the main independent variable).Once we obtain a satisfactory regression model, discuss potential sources of bias - e.g., not using the correct functional form, measurement error, omitted variable bias, the simultaneity problem, etc.Finally, what are some key considerations in selecting "control" variables in a multiple regression analysis.

Answer: What is required to be able to say that a particular independent variable, or treatment,causes the dependent variable?

A requirement to state that an independent variable or treatment causes the dependent variable could be defined as an instrumental variable.According to chapter 8 an instrumental variable "in the context of regression analysis" is a variable that helps to isolate and help demonstrate that the treatment causes the effect but does not correlate directly with the outcome.Now for me I am not sure this helps define totally what is "required" but it does continue to drive home the importance of understanding that many things can affect the outcome.The importance is being able to isolate treatments that directly impact the dependent variable, while identifying, isolating and dismissing items that have a type of correlation to the dependent variable.For example, if we were trying to identify the complex problem of money causes happiness.We would have to ensure that our experiment isolates items like mental health, family status or living situations which all may also corelate to "happiness".So, if we have a sample of people and can ensure the sample represents all those individuals that are of sound mental health, all live at a certain level currently, and are happy with their current living situation, we might be able to tell if higher salaries make them happy, but it could easily be another factor that we failed to identify.But to ensure that money does "make" them happy we would need to clearly show that those that made more money were happier, than those that stayed at the same income level, or dropped.

In the above "experiment" the random sample and random assignment would be defined as follows.The random sample would have to ensure that the working aged population say 18-65 are fairly represented by race and gender.Many other factors contribute to this, but in order to keep things simple I am staying within these parameters.Numbers would have to be gathered, difficult for sure, to identify how many of each are in the United States so a random sample could be gathered fitting the total population but scaled to fit the experiment.First the numbers, according to the most recent (2019) census quick facts, 50.8% of the US population is female and 49.2% are male.Further 55.2% fall into the category of 18-65 years old in 2019.Using a total population of 328,239,523 then 166,745,677 would be female within our 18-65 and 161,493,845 are male.So now we would need to put them into a sample that makes sense.We can use 5000 samples, and of the 5000 samples 50.8% will need to be female and 49.2% would need to be male.This random sample taken from the total population would best represent the entire nation.The problem of bias could arise if we take the sample 5000 from one state or three states for example, which could be further complicated by choosing states that are primarily Democrat or Republican.

Random assignment would be identified in this experiment as 50% of the females and 50% of the males each get instant raises or salary increases and the other half either stay the same or drop by a percentage regarding monthly salary.