Causal Analytics & AB Testing (3) (1)

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Feb 20, 2024

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Question 1: What should HMRC write its letter to delinquent taxpayers? Why do you think your proposal would be effective? The HMRC could send a proposed letter which would look like this: Dear [Taxpayer’s Name], We hope this letter finds you well. We understand that managing your finances can sometimes be challenging, and we are here to help you resolve your outstanding tax matter. As of [current date], we have not received your tax payment of [amount], which is due for the tax year [specify the year]. Your prompt attention to this matter is crucial to avoid any further complications. We want to work with you to avoid costly and harsh implications, including penalties, interest charges, and legal action. We understand that life circumstances can be challenging, and we are prepared to offer your assistance. Please contact our dedicated tax helpline at [phone number] immediately to discuss your situation, explore possible payment arrangements, or address any questions or concerns you may have. Our goal is to help you find a manageable solution and ensure that you meet your tax obligations promptly. We believe that by working together, we can resolve this matter in a way that is both fair and beneficial to you. We look forward for your prompt response and assisting you in settling your tax liability. Sincerely, Her Majesty's Revenue and Customs (HMRC) This proposal may be effective for several key reasons. It maintains empathy while conveying the seriousness of the situation and the potential implications of non-compliance. It encourages prompt action to avoid adverse consequences and underscores HMRC's willingness to collaborate for a favorable resolution. Lastly, the personalization of addressing John by name enhances the letter's effectiveness by making it more relatable. Overall, this proposal harnesses behavioral insights to encourage John Smith to fulfill his overdue tax obligations, offering support and alternatives to punitive measures.

Question 2: How should HMRC evaluate the success of the proposed letter? Explain. To evaluate the impact of the new tax letter, HMRC should consider the importance of Randomized Controlled Trials (RCTs) and causality analysis. RCTs are pivotal in establishing causality by randomly assigning delinquent taxpayers to either the treatment group (those receiving the letter) or the control group (those not receiving it). Randomization ensures that both groups are comparable except for receiving the letter. By comparing the payment behavior of these groups, HMRC can confidently attribute any observed differences to the letter itself, effectively isolating its causal impact. Directly assessing payment compliance is essential for evaluating success. HMRC can analyze tax payment statuses before and after sending the letter, directly measuring its effectiveness in boosting compliance. Additionally, the timeliness of responses can be scrutinized to determine if the letter imparts a sense of urgency and drives prompt action. Economic efficiency can be assessed through a cost-benefit analysis, weighing the expenses incurred in sending the letters against the additional tax revenue generated due to improved compliance. Segmentation analysis enables HMRC to determine if the letter is more effective for specific taxpayer groups, helping tailor future communications. Behavioral insights metrics, such as references to social norms in the letter, indicate its impact on influencing taxpayer behavior. Operational metrics, including the volume of inquiries received, help HMRC understand resource allocation and workload, informing process adjustments. Case studies and interviews with responsive taxpayers can provide deeper insights into why the letter was effective for specific individuals. Finally, ethical considerations are essential, necessitating an evaluation of unintended consequences, particularly for vulnerable taxpayers, and making necessary adjustments to the letter and process. Continual testing and refinement of letter variations are vital to identify the most effective messaging and strategies. This systematic approach allows HMRC to make informed, data-driven decisions about the future role of the letter in their tax compliance strategy.

Question 3: Causal Question (1): What is the impact of stay-at-home orders due to COVID-19 on economic activity in a state? (a) Treatment and the outcome variable: Economic activity is the key outcome variable, and it can be assessed using a number of metrics, including GDP, employment rates, consumer spending, and firm closures. Treatment: The "treatment" in this sense refers to the issuance of COVID-19 stay-at-home orders, which would have an effect on how people and businesses behave in the state. (b) Sources of Bias :  Stay-at-home orders may elicit diverse reactions from people and businesses in various states. For instance, distinct economic effects can be felt in states with a higher proportion of vital workers or in industries that can work remotely.There may be potential bias due to differences in state characteristics including population density, demography, and economic structure.  Economic effects might not be felt right away. Drawing inferences from short-term data may not fully reflect the impact since short-term and long-term effects may differ.  Factors other than stay-at-home orders can influence economic activity, such as government stimulus measures, public compliance with orders, and the severity of the COVID-19 outbreak.  Other factors, such as government stimulus measures, public compliance with directives, and the intensity of the COVID-19 outbreak, can have an impact on economic activity. To effectively address these sources of bias, rigorous research methods like a randomized controlled trial or a quasi-experimental design are necessary. Causal Question (2): Does sponsored search advertising on Google affect clicks for a website? (a) Outcome Variable and Treatment: The number of website clicks is the outcome variable that is of interest to us. It gauges how well an advertising campaign performed in bringing visitors to the website. Treatment: The term "treatment" in this context refers to Sponsored search advertising on Google. This treatment involves running paid ads on Google's search engine results pages. (b) Sources of Bias:  Website owners may categorically differ from those who do not advertise when it comes to running sponsored search advertising on Google. Selection bias can be caused by factors such as the website's content, industry, or its marketing plan. It might be biased to compare click-through rates between advertisers and non-advertisers directly.  The quality of the ads, the relevance of keywords, and ad copy can vary among advertisers. Differences in ad quality can lead to variations in click-through rates, independent of the advertising platform.  The traffic to a website might be impacted by seasonal trends, modifications in customer behavior, or outside occurrences. Inadequate consideration of these elements in an analysis could lead to false results.  The analysis may be biased by omitting important variables, such as competitor advertising efforts or the overall marketing plan for the website. By randomly assigning people or websites to treatment or control groups, we can overcome the problems caused by selection bias and omitted variable bias. Research methodologies like a randomized controlled trial or a quasi-experimental design should be utilized.

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Question 4: a) What is the null hypothesis here? Null Hypothesis for this experiment will be H0: There is no discrimination in the proportion of receiving callbacks between resumes with traditionally African American names and traditionally white names . b) What is the difference in means for the two groups? Difference in means = Proportion of receiving callbacks for white-sounding names - Proportion of receiving callbacks for African American names = 0.12 - 0.04 = 0.08 c) Calculate the t-statistic necessary to test the null. Show the formula you use. What is the p- value of your test statistic? Using the above R code, we got t-value = 16.33 and p-value = 0. d) Do you reject the null? If the p-value is less than or equal to alpha (0.05), we will reject the null hypothesis. In this case, with a p-value of almost 0, we would reject the null hypothesis. e) What do you conclude about discrimination in labor markets? We can draw the conclusion that there is significant evidence of discrimination in the labor markets based on the analysis. The null hypothesis was rejected, indicating that resumes with traditionally African American names were considerably less likely to be called back than resumes with usually white names. This implies that there may be prejudice or discrimination in the hiring process based on the perceived ethnicity of the applicant's names.

Question 5: Using the r code below, we can determine that the total number of impressions per group needed to indicate a 3% improvement in conversion rate is 8,833,320 impressions. > pwr.2p.test(h=ES.h(p1=0.002,p2=.002+0.002*.03),sig.level=0.05,power=0.8) Results: h = 0.00133308 n = 8833320 sig.level = 0.05 power = 0.8 alternative = two.sided NOTE: same sample sizes

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