{'fraction_non_alphanumeric': 0.013605442176870748, 'fraction_numerical': 0.0, 'mean_word_length': 5.923529411764706, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Efficiency and robustness of causal effect estimators when noncompliance is measured with error

Estimating causal effects from randomized controlled trials is often complicated due to participant noncompliance to randomized treatment. Although there are a variety of methods to estimate causal effects in the presence of noncompliance, they generally make the assumption that noncompliance is measured without error. This is frequently an untenable assumption, particularly when noncompliance is based on participant self‐report. To overcome this issue, we treat compliance as an unobserved variable and show how to estimate the probability of compliance given a biomarker of treatment and the other observed data. We present inverse probability weighted estimators, regression‐based estimators, and a doubly‐robust augmented estimator that rely on the estimated probability of compliance rather than an indicator of compliance. We investigate the finite‐sample properties of the estimators and their efficiency and robustness under correctly specified or misspecified models, and we apply the estimators to a recently completed trial of very low nicotine content cigarettes.